From 997276cef5a22cfefb0ae6213ee6875c85e22b4f Mon Sep 17 00:00:00 2001 From: Koen van Greevenbroek Date: Mon, 20 Jul 2026 18:04:23 -0700 Subject: [PATCH 01/12] feat: consumption-basis water supply with AWARE scarcity and groundwater Replace the single per-region growing-season water store, sized from Huang et al. withdrawals, with a consumption-basis water system. Crops draw their GAEZ net irrigation requirement E from per-region field buses; a delivery link draws C = E / eta_c from the regional pool, with eta_c calibrated at build time against observed WaterGAP irrigation consumption; withdrawal W is reported via the consumed fraction. The three quantities the literature routinely conflates are now distinct. Availability, monthly timing and within-region basin allocation come from WaterGAP 2.2e; AWARE 2.0 supplies the convex scarcity curve. Water supply and demand can be resolved at intra-year periods (water.temporal_resolution, a divisor of 12), so a season whose surface cannot meet its demand draws groundwater endogenously instead of being rescued by annual averaging. Demand is placed into periods by the observed MIRCA-OS irrigated crop calendar, retimed by iterative proportional fitting to WaterGAP's monthly requirement. Renewable and non-renewable groundwater bands are annual per-region resources (an aquifer can be pumped in any period) sitting additively on top of the period-bound surface supply, so mining emerges endogenously rather than being capped at renewable availability. The supply.mode ladder (single/scarcity/groundwater) is retired in favour of two independent switches, supply.scarcity_tiers and supply.groundwater: the ladder welded two orthogonal decisions to one enum and had no rung for collapsed tiers with the groundwater split, which is what main wants by default. The groundwater bands are derived from the pre-collapse curve, since renewable groundwater borrows the region's scarcest surface CF and collapsed tiers carry none. Defaults are chosen for cost, not fidelity: annual resolution and collapsed tiers, with the groundwater split kept. Both caveats are documented -- at annual resolution the whole year's pool serves every season, so the groundwater bands go nearly inert and reported depletion is an artefact of the resolution; and the AWARE pool is a looser constraint than the previous binding present-day withdrawal cap, so results move. tests/config/test.yaml pins quarterly resolution with tiers and groundwater on so CI still covers the full-fidelity path. New solve-time levers, both off by default: water_scarcity and groundwater_depletion pricing/capping. Scarcity pricing raises if scarcity_tiers is off rather than silently pricing an identically zero signal. Analysis gains per-region water metrics. Also fixes a GAEZ artefact where a few cells carry a negative net irrigation requirement, which flipped those crop links into spurious water producers, and removes the dead Water Footprint Network availability path along with the build inputs that went unused when the per-region water store was replaced. --- CHANGELOG.md | 38 ++ config/default.yaml | 114 +++- config/schemas/config.schema.yaml | 152 +++++- data/curated/mirca_os_calendar_supplement.csv | 15 + docs/configuration.rst | 9 +- docs/crop_production.rst | 75 +-- docs/data_sources.rst | 27 +- docs/index.rst | 1 + docs/validation.rst | 2 +- docs/water.rst | 441 +++++++++++++++ docs/workflow.rst | 40 +- tests/config/test.yaml | 9 + tests/test_crop_calendar_retiming.py | 171 ++++++ tests/test_crop_costs.py | 10 +- tests/test_integration.py | 32 +- tests/test_water_groundwater.py | 330 +++++++++++ tests/test_water_surface.py | 165 ++++++ tests/test_water_temporal.py | 172 ++++++ workflow/rules/crops.smk | 8 + workflow/rules/model.smk | 29 +- workflow/rules/retrieve.smk | 93 +++- workflow/rules/water.smk | 224 ++++++-- workflow/scripts/analysis/analyze_model.py | 6 + .../analysis/extract_objective_breakdown.py | 12 + .../scripts/analysis/extract_water_metrics.py | 150 +++++ workflow/scripts/build_crop_yields.py | 6 +- workflow/scripts/build_mirca_crop_calendar.py | 431 +++++++++++++++ workflow/scripts/build_model.py | 78 ++- workflow/scripts/build_model/crops.py | 181 +++--- .../scripts/build_model/infrastructure.py | 51 +- workflow/scripts/build_model/irrigation.py | 213 ++++++++ .../scripts/build_model/primary_resources.py | 230 +++++++- workflow/scripts/build_model/utils.py | 1 + workflow/scripts/build_multi_cropping.py | 153 +++++- .../build_region_water_availability.py | 353 ------------ workflow/scripts/build_region_water_aware.py | 515 ++++++++++++++++++ workflow/scripts/build_region_watergap.py | 308 +++++++++++ workflow/scripts/compose_water_supply.py | 247 +++++++++ .../scripts/process_huang_irrigation_water.py | 5 +- workflow/scripts/solve_model/core.py | 125 +++++ workflow/scripts/solve_namespace.py | 11 + workflow/scripts/water_periods.py | 123 +++++ 42 files changed, 4711 insertions(+), 645 deletions(-) create mode 100644 data/curated/mirca_os_calendar_supplement.csv create mode 100644 docs/water.rst create mode 100644 tests/test_crop_calendar_retiming.py create mode 100644 tests/test_water_groundwater.py create mode 100644 tests/test_water_surface.py create mode 100644 tests/test_water_temporal.py create mode 100644 workflow/scripts/analysis/extract_water_metrics.py create mode 100644 workflow/scripts/build_mirca_crop_calendar.py create mode 100644 workflow/scripts/build_model/irrigation.py delete mode 100644 workflow/scripts/build_region_water_availability.py create mode 100644 workflow/scripts/build_region_water_aware.py create mode 100644 workflow/scripts/build_region_watergap.py create mode 100644 workflow/scripts/compose_water_supply.py create mode 100644 workflow/scripts/water_periods.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 9274aa31..954ec5f7 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -17,6 +17,9 @@ introduce breaking changes to configuration and outputs. ### Added +- Fixed a GAEZ data artefact where a handful of cells carry a negative net + irrigation requirement, which flipped those crop links into spurious water + *producers*. Negative requirements are now clipped to zero. - Multiple cropping is now anchored to an observed baseline derived from MIRCA-OS v2 (new automated data source), using the available 2010, 2015, or 2020 release nearest `baseline_year`. A fixed, documented sequence catalog @@ -94,6 +97,41 @@ introduce breaking changes to configuration and outputs. resource-class cell coverage once per configuration and reuse it across crops, substantially reducing build time and peak memory without changing outputs. +- **The water system has been rebuilt on a consumption basis.** Irrigation + previously drew from a single per-region growing-season store sized from + Huang et al. withdrawals. It now draws from a regional pool anchored on + WaterGAP 2.2e irrigation consumption, through a per-region delivery link + whose efficiency `eta_c` is calibrated at build time against observed + consumption, with availability and scarcity characterised by AWARE 2.0. The + three water quantities the literature conflates (crop net requirement, + consumption, withdrawal) are now distinct and separately reported. New + automatic downloads: AWARE 2.0 and WaterGAP 2.2e (ISIMIP3a). The Water + Footprint Network "sustainable" supply scenario and the + `water.supply_scenario` key are removed; the source is now + `water.data.availability` (`aware` or `current_use`), defaulting to `aware`. + **This is a results-affecting default change** — the AWARE pool is a looser + constraint than the previous binding present-day withdrawal cap. +- Water supply and demand can be resolved at **intra-year periods** + (`water.temporal_resolution`, a divisor of 12), so a season whose surface + cannot meet its demand draws groundwater endogenously instead of being + rescued by annual averaging. Crop water demand is placed into periods by the + observed MIRCA-OS irrigated crop calendar, retimed to WaterGAP's monthly + requirement. **The default is 1 (annual), which is cheap but has a + consequence worth stating plainly: at annual resolution the groundwater bands + are nearly inert and reported depletion falls to near zero — an artefact of + the resolution, not a finding.** Studies about water should raise it. +- Water supply fidelity is now two independent switches rather than a ladder: + `water.supply.scarcity_tiers` (convex AWARE scarcity tiers, default off — + each region-period pool is one flat availability cap) and + `water.supply.groundwater` (additive annual renewable and non-renewable + groundwater bands, default on). All four combinations are valid. Scarcity + pricing or capping now requires `scarcity_tiers` and raises otherwise, since + with collapsed tiers there is no scarcity signal to price. +- New optional solve-time levers, both off by default: `water_scarcity` + (pricing and/or capping accumulated AWARE scarcity) and + `groundwater_depletion` (pricing and/or capping accumulated mining). Analysis + gains a `water_metrics` output with per-region withdrawal, scarcity, + renewable groundwater and depletion. - Model regions are now built **basin-aware**: GADM provinces are first split along AWARE hydrological basin boundaries, and each country is partitioned into regions balancing geography against basin scarcity diff --git a/config/default.yaml b/config/default.yaml index 387fa796..70a0c8a0 100644 --- a/config/default.yaml +++ b/config/default.yaml @@ -93,6 +93,7 @@ numerics: # Coefficient clipping: zero negligible coefficients on retained components. min_link_area_mha: 0.000001 # ~1 ha; smaller land areas -> 0 min_water_requirement_m3_per_ha: 0.1 # <0.1 mm/ha irrigation -> no water draw + min_water_capacity_mm3: 0.000001 # <1 m3 annual/period capacity -> no supply link min_co2_coefficient_tco2_per_ha: 0.001 # near-zero land carbon flux -> 0 min_cost_correction_bnusd: 0.000001 # ~0.001 USD per unit flow -> 0 @@ -250,14 +251,111 @@ land: # --- section: water --- water: - # Water supply scenario determines which dataset is used for regional water limits: - # - "sustainable": Water Footprint Network blue water availability by basin (Hoekstra & Mekonnen 2011) - # Represents sustainable water extraction limits. - # - "current_use": Huang et al. (2018) gridded irrigation water withdrawals - # Represents actual/current agricultural water use, useful for validation. - supply_scenario: "current_use" - # Reference year for Huang irrigation data (only used when supply_scenario is "current_use") - huang_reference_year: 2010 + # Number of intra-year periods the LP resolves for water supply and demand + # (structural; must divide 12). Each period balances its own surface + # availability against the crop water demand whose growing season falls in it, + # so a season whose surface cannot meet its demand draws groundwater + # endogenously. 1 = annual, 12 = monthly, 4 = quarterly. + # + # The default is 1: cheap, and adequate wherever water is not the object of + # study. Note what it costs. At 1 a region's whole annual surface pool is + # available to every season, so wet-season surplus subsidises the dry season + # and the groundwater bands go nearly inert -- a near-zero reported depletion + # at T=1 is an artefact of the resolution, not a finding. Studies about water + # should raise this (4 is a reasonable seasonality/solve-time compromise). + temporal_resolution: 1 + # Structural (build-time) water-supply configuration. The two switches below + # are independent: tier resolution and the groundwater split can be varied + # separately. + supply: + # Convex AWARE scarcity tiers per region-period, so drawing more water costs + # more where water is scarcer. False collapses each region-period pool to one + # flat cap -- a plain availability constraint, cheaper and sufficient where + # water is not the object of study. Scarcity pricing (see water_scarcity) + # requires this. + scarcity_tiers: false + # Additive renewable- and non-renewable-groundwater bands (annual, per + # region) on top of the period-bound surface supply, so mining emerges + # endogenously wherever surface plus renewable groundwater fall short. + groundwater: true + # Marginal cost of non-renewable groundwater (USD per m3): a small pumping- + # energy cost (~100 m lift at ~0.1 USD/kWh) that also orders groundwater last + # in the merit order. Only active when groundwater is true. + pumping_cost_usd_per_m3: 0.04 + # Non-renewable groundwater ceiling as a multiple of a region's annual + # agricultural consumption. Generous and non-binding: mining is drawn only to + # cover the residual deficit (pumping cost keeps it minimal), so this bounds + # mining well above any plausible draw without pinning it. + groundwater_ceiling_factor: 3.0 + # Data provenance (build-time). Swapping a source changes numbers, not model + # behaviour; every source yields the same consumption-basis regional pool. + data: + # Regional water availability + scarcity pool: "aware" (AWARE2.0) or + # "current_use" (Huang et al. gridded irrigation withdrawals; validation). + availability: "aware" + # Groundwater band source (storage-decline depletion + renewable irrigation + # groundwater consumption); used only when supply.groundwater is true. + groundwater: "watergap" + # Inclusive reference window for WaterGAP irrigation consumption, surface + # delivery and the eta_c anchor. Matches AWARE2.0's recent scarcity window. + surface_reference_start: 1990 + surface_reference_end: 2019 + # Inclusive year window for the groundwater-storage depletion trend. This + # stays recent so the depletion band represents current mining. + groundwater_trend_start: 2000 + groundwater_trend_end: 2019 + # Reference year for Huang irrigation data (only when availability is "current_use"). + huang_reference_year: 2010 + # Consumption-basis irrigation delivery. The water pool, scarcity and + # groundwater bands sit on consumption C; crops need the GAEZ net requirement + # E. A per-region delivery link draws C = E / eta_c from the pool, with eta_c + # calibrated at build time as E_baseline / C_anchor (clipped to + # [eta_min, eta_max] and floored at E_baseline / pool for baseline + # feasibility). The anchor is WaterGAP irrigation consumption (pirruse) -- + # one volume basis for supply envelope, groundwater bands and demand. + irrigation: + # Lower clip on the calibrated consumptive efficiency; ratios below this + # are treated as data-quality issues (logged), not real inefficiency. + eta_min: 0.2 + # Upper clip. Values above 1 encode deficit irrigation: observed + # consumption below the GAEZ full requirement (India, Pakistan, Thailand), + # so the baseline draw matches observed consumption instead of the + # requirement. The cap guards regions with unreliably small consumption + # anchors, where an uncapped ratio would make marginal irrigation free. + eta_max: 5.0 + # Consumed fraction C/W (consumption over withdrawal). Used to report + # withdrawal in the analysis and to convert the current_use availability + # pool from withdrawal to consumption basis. Global constant for now. + consumed_fraction: 0.58 + +# --- section: water_scarcity --- +# Solve-time pricing and/or capping of the accumulated AWARE water-scarcity +# impact (Mm3 world-equivalent). Both default off; the availability cap from the +# supply curve is always active. Requires water.supply.scarcity_tiers, since with +# collapsed tiers every surface characterisation factor is zero and there is no +# scarcity signal to price. +water_scarcity: + pricing_enabled: false + price: 0 # Shadow price, USD per m3 world-equivalent + cap_mm3_world_eq: null # Epsilon-constraint cap (Mm3 world-eq); null = no cap + # CF applied to non-renewable groundwater under scarcity pricing: each mined + # m3 is charged nonrenewable_cf * price. AWARE covers renewable water only and + # excludes fossil stocks, so this is a bridging weight that prices a mined m3 + # at the scarcity of the exhausted renewable water it displaces, closing the + # otherwise free substitution into fossil groundwater. 100 is AWARE's + # demand-exceeds-availability cutoff plus a non-renewability premium. Set null + # to leave mining unpriced here, e.g. when groundwater_depletion prices or caps + # it separately (combining both pricings is an error). + nonrenewable_cf: 100 + +# --- section: groundwater_depletion --- +# Solve-time pricing and/or capping of accumulated non-renewable groundwater +# depletion (Mm3 mined). Active only when water.supply.groundwater is true; both +# default off. +groundwater_depletion: + pricing_enabled: false + price: 0 # Shadow price, USD per m3 mined + cap_mm3: null # Epsilon-constraint cap (Mm3 mined); null = no cap # --- section: fertilizer --- fertilizer: diff --git a/config/schemas/config.schema.yaml b/config/schemas/config.schema.yaml index d600e6e1..62ec2706 100644 --- a/config/schemas/config.schema.yaml +++ b/config/schemas/config.schema.yaml @@ -190,6 +190,10 @@ properties: type: number minimum: 0 description: "Land areas (p_nom_max, baseline) below this many Mha are zeroed" + min_water_capacity_mm3: + type: number + minimum: 0 + description: "Water supply bands below this capacity (Mm3) are dropped" min_water_requirement_m3_per_ha: type: number minimum: 0 @@ -407,21 +411,145 @@ properties: water: type: object - required: [supply_scenario, huang_reference_year] + required: [temporal_resolution, supply, data, irrigation] additionalProperties: false properties: - supply_scenario: - type: string - enum: [sustainable, current_use] - description: | - Water supply scenario determining which dataset is used for regional water limits: - - 'sustainable': Water Footprint Network blue water availability (Hoekstra & Mekonnen 2011) - - 'current_use': Huang et al. (2018) gridded irrigation water withdrawals - huang_reference_year: + temporal_resolution: type: integer - minimum: 1971 - maximum: 2010 - description: "Reference year for Huang irrigation data (only used when supply_scenario is 'current_use')" + enum: [1, 2, 3, 4, 6, 12] + description: | + Structural number of intra-year water periods the LP resolves (a + divisor of 12). Each period balances its own surface availability + against the crop water demand whose growing season falls in it, so + seasonal shortfalls draw groundwater endogenously. 1 = annual (no + seasonal binding); 12 = monthly (faithful); intermediate values + group whole months into equal blocks. Cost scales ~linearly in the + value on the water side of the model. + supply: + type: object + required: [scarcity_tiers, groundwater, pumping_cost_usd_per_m3, groundwater_ceiling_factor] + additionalProperties: false + properties: + scarcity_tiers: + type: boolean + description: | + Keep the convex AWARE scarcity tiers per region-period (true), or + collapse each region-period pool to one flat availability cap + (false). Required for water_scarcity pricing or capping, which has + no signal to act on once every surface CF is zero. + groundwater: + type: boolean + description: | + Emit additive annual renewable- and non-renewable-groundwater + bands on top of the period-bound surface supply, so mining emerges + endogenously wherever surface plus renewable groundwater fall + short. Independent of scarcity_tiers. + pumping_cost_usd_per_m3: + type: number + minimum: 0 + description: "Marginal cost of non-renewable groundwater (USD per m3); merit-order separator, active only when groundwater is true" + groundwater_ceiling_factor: + type: number + minimum: 0 + description: "Non-renewable groundwater ceiling as a multiple of annual agricultural consumption; generous and non-binding, active only when groundwater is true" + data: + type: object + required: [availability, groundwater, surface_reference_start, surface_reference_end, groundwater_trend_start, groundwater_trend_end, huang_reference_year] + additionalProperties: false + properties: + availability: + type: string + enum: [aware, current_use] + description: | + Regional water availability + scarcity pool source: + - 'aware': AWARE2.0 (Seitfudem et al. 2025, WaterGAP2.2e) convex scarcity curve and native basin geometry; pool volumes, monthly timing and within-region basin allocation set by the WaterGAP surface field + - 'current_use': Huang et al. (2018) gridded irrigation water withdrawals + groundwater: + type: string + enum: [watergap] + description: "Groundwater band source (depletion + renewable net abstraction); used only when supply.groundwater is true" + surface_reference_start: + type: integer + minimum: 1901 + maximum: 2019 + description: "First year (inclusive) of the WaterGAP irrigation-consumption and surface-delivery reference window" + surface_reference_end: + type: integer + minimum: 1901 + maximum: 2019 + description: "Last year (inclusive) of the WaterGAP irrigation-consumption and surface-delivery reference window" + groundwater_trend_start: + type: integer + minimum: 1901 + maximum: 2019 + description: "First year (inclusive) of the groundwater-storage depletion trend window" + groundwater_trend_end: + type: integer + minimum: 1901 + maximum: 2019 + description: "Last year (inclusive) of the groundwater-storage depletion trend window" + huang_reference_year: + type: integer + minimum: 1971 + maximum: 2010 + description: "Reference year for Huang irrigation data (only used when availability is 'current_use')" + irrigation: + type: object + required: [eta_min, eta_max, consumed_fraction] + additionalProperties: false + properties: + eta_min: + type: number + exclusiveMinimum: 0 + maximum: 1 + description: "Lower clip on the calibrated consumptive irrigation efficiency eta_c = E_baseline / C" + eta_max: + type: number + minimum: 1 + description: "Upper clip on eta_c; values above 1 encode deficit irrigation (observed consumption below the GAEZ requirement)" + consumed_fraction: + type: number + exclusiveMinimum: 0 + maximum: 1 + description: "Consumed fraction C/W (consumption over withdrawal); reporting and current_use pool conversion" + + water_scarcity: + type: object + required: [pricing_enabled, price, cap_mm3_world_eq, nonrenewable_cf] + additionalProperties: false + properties: + pricing_enabled: + type: boolean + description: "Whether to price accumulated water scarcity in the objective" + price: + type: number + minimum: 0 + description: "Water-scarcity shadow price, USD per m3 world-equivalent" + cap_mm3_world_eq: + type: [number, "null"] + minimum: 0 + description: "Epsilon-constraint cap on total water scarcity (Mm3 world-eq); null disables the cap" + nonrenewable_cf: + type: [number, "null"] + minimum: 0 + description: "CF charged per m3 of non-renewable groundwater under scarcity pricing (AWARE cutoff 100); null leaves mining unpriced here" + + groundwater_depletion: + type: object + required: [pricing_enabled, price, cap_mm3] + additionalProperties: false + properties: + pricing_enabled: + type: boolean + description: "Whether to price accumulated non-renewable groundwater depletion in the objective" + price: + type: number + minimum: 0 + description: "Groundwater-depletion shadow price, USD per m3 mined" + cap_mm3: + type: [number, "null"] + minimum: 0 + description: "Epsilon-constraint cap on total groundwater depletion (Mm3 mined); null disables the cap" fertilizer: type: object diff --git a/data/curated/mirca_os_calendar_supplement.csv b/data/curated/mirca_os_calendar_supplement.csv new file mode 100644 index 00000000..b39ed93f --- /dev/null +++ b/data/curated/mirca_os_calendar_supplement.csv @@ -0,0 +1,15 @@ +# Calendar-only additions to mirca_os_crop_mapping.csv: MIRCA-OS classes whose +# monthly growing-area grids feed the irrigated crop calendar +# (build_mirca_crop_calendar) but which are excluded from the multi-cropping +# concordance (long-duration/ratoon crops, unmappable aggregates). A MIRCA +# class may map to several GLADE crops (all receive the same monthly profile; +# the class's growing area is split evenly between them for weighting). +mirca_crop,glade_crop +Sugar cane,sugarcane +Pulses,dry-pea +Pulses,chickpea +Pulses,cowpea +Pulses,gram +Pulses,phaseolus-bean +Pulses,pigeonpea +Fodder,alfalfa diff --git a/docs/configuration.rst b/docs/configuration.rst index 499e1332..269c8692 100644 --- a/docs/configuration.rst +++ b/docs/configuration.rst @@ -640,8 +640,13 @@ Water Supply :start-after: # --- section: water --- :end-before: # --- section: fertilizer --- -* ``water.supply_scenario`` selects the water availability dataset: ``sustainable`` (Water Footprint Network blue water availability) or ``current_use`` (Huang et al. irrigation withdrawals). Use ``current_use`` for validation or benchmarking against present-day withdrawals. -* ``water.huang_reference_year`` selects the year (1971-2010) used for the Huang monthly withdrawals when ``supply_scenario`` is ``current_use``. +* ``water.temporal_resolution`` sets the number of intra-year periods the LP resolves for water (a divisor of 12). The default 1 is annual and cheap; raise it (4 is a reasonable compromise) for any study about water, since at annual resolution the whole year's pool serves every season and the groundwater bands go nearly inert. See :doc:`water`. +* ``water.supply.scarcity_tiers`` keeps the convex AWARE scarcity tiers (true) or collapses each region-period pool to one flat availability cap (false, the default). Required for ``water_scarcity`` pricing or capping. +* ``water.supply.groundwater`` adds annual renewable and non-renewable groundwater bands on top of the period-bound surface supply (default true), so mining emerges endogenously where surface falls short. +* ``water.data.availability`` selects the water availability dataset: ``aware`` (AWARE 2.0 scarcity curve on WaterGAP volumes, the default) or ``current_use`` (Huang et al. irrigation withdrawals, for validation against present-day use). +* ``water.data.huang_reference_year`` selects the year (1971-2010) used for the Huang monthly withdrawals when ``availability`` is ``current_use``. +* ``water.irrigation.eta_min`` / ``eta_max`` clip the build-time consumptive-efficiency calibration; ``consumed_fraction`` is the consumption-over-withdrawal ratio used for reporting. +* ``water_scarcity`` and ``groundwater_depletion`` are solve-time pricing/capping levers, both off by default. .. literalinclude:: ../config/default.yaml :language: yaml diff --git a/docs/crop_production.rst b/docs/crop_production.rst index 02b97e9e..9b293ca4 100644 --- a/docs/crop_production.rst +++ b/docs/crop_production.rst @@ -344,63 +344,14 @@ For comprehensive details on crop production cost data sources, processing metho Water Constraints ----------------- -For irrigated crops, water availability is a key constraint. The model supports two water supply scenarios, selected via ``config.water.supply_scenario``: - -* ``sustainable``: Water Footprint Network blue water availability by basin, representing sustainable extraction limits. -* ``current_use``: Huang et al. monthly irrigation withdrawals, representing present-day agricultural water use (useful for validation). - -Both scenarios are processed into the same regional monthly and growing-season CSVs. ``workflow/rules/water.smk`` selects the configured scenario and writes the unified outputs under ``processing/{name}/water/`` for model building. - -Sustainable Basin-Level Availability -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The model uses the Water Footprint Network's monthly blue water availability dataset for 405 GRDC river basins [hoekstra2011]_. - -Processing steps (``workflow/scripts/process_blue_water_availability.py``): - -1. **Load basin shapefile** with monthly availability (Mm³/month) -2. **Aggregate by basin and month** to get monthly water budgets - -.. figure:: https://github.com/Sustainable-Solutions-Lab/GLADE/releases/download/doc-figures/water_basin_availability.png - :width: 100% - :alt: Basin water availability map - - Annual blue water availability by GRDC river basin (mm/year). The map shows area-normalized yearly water availability across 405 major river basins globally. Higher availability is shown in darker blue, allowing direct comparison between basins of different sizes. While we normalize by area for better visualisation here, GLADE tracks total water amount availability internally. - -Current-Use Irrigation Withdrawals -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -When ``water.supply_scenario`` is set to ``current_use``, the workflow uses Huang et al. (2018) gridded monthly irrigation withdrawals (0.5 degree resolution, 1971-2010) [huang2018]_. ``workflow/scripts/process_huang_irrigation_water.py`` aggregates these withdrawals to regions and computes growing-season totals using the same crop-weighted method as the sustainable dataset. - -Outputs: - -* ``processing/{name}/water/current_use/monthly_region_water.csv`` -* ``processing/{name}/water/current_use/region_growing_season_water.csv`` - -Regional Water Assignment -~~~~~~~~~~~~~~~~~~~~~~~~~ - -Blue water availability is allocated to optimization regions using the dataset-specific processing scripts: - -* ``workflow/scripts/build_region_water_availability.py`` for ``sustainable`` -* ``workflow/scripts/process_huang_irrigation_water.py`` for ``current_use`` - -Both produce the same output schema so the model can remain unchanged. - -For the sustainable dataset, the allocation steps are: - -1. **Spatial join**: Intersect region polygons with basin polygons -2. **Area weighting**: Allocate basin water proportional to overlap area -3. **Growing season matching**: Assign water to regions based on when crops are growing - - * Uses growing season start/length from GAEZ - * Sums monthly availability over the growing period - * For now, this is done on average over all crops that can grow in the region - -4. **Output**: CSV files: - - * ``processing/{name}/water/monthly_region_water.csv``: Monthly water by region - * ``processing/{name}/water/region_growing_season_water.csv``: Growing season totals +For irrigated crops, water availability is a key constraint. Water is a primary +resource in its own right: the crop-production link consumes the crop's net +irrigation requirement :math:`E` (GAEZ ``RES05-WDC``, m3/ha) from a per-region +*field* bus, which a calibrated efficiency link feeds from the regional +consumption pool. The pool, the AWARE scarcity characterisation, the groundwater +bands, the three water quantities (net requirement, consumption, withdrawal) and +the solve-time pricing/capping levers are documented in the dedicated +:doc:`water` chapter. .. figure:: https://github.com/Sustainable-Solutions-Lab/GLADE/releases/download/doc-figures/water_region_availability.png :width: 100% @@ -534,6 +485,16 @@ modeled. The ``multiple_cropping`` config section may set a catalog name to sequences have an implicit zero baseline and expose only GAEZ-constrained optimization potential; catalog entries cannot be redefined in config. +**Seasonal water split.** When the model resolves intra-year water periods +(``water.temporal_resolution`` > 1), each irrigated cycle's net requirement is +placed into the periods by that crop's observed MIRCA-OS irrigated calendar for +the cell's region, rather than smeared evenly across the year -- so a dry-season +cycle (rabi wheat, boro rice) lands its demand in the scarce period. Where a +region has no observed calendar for the crop, the cycle falls back to its own +GAEZ growing season; repeated same-crop cycles, for which GAEZ gives a single +window, are staggered at equal ``365/n``-day offsets in that fallback so the +second cycle does not double the monsoon window. + The RES01 classes report the agro-climatic zone the pixel belongs to. We interpret the numeric codes as: diff --git a/docs/data_sources.rst b/docs/data_sources.rst index 4efae5bf..e280d259 100644 --- a/docs/data_sources.rst +++ b/docs/data_sources.rst @@ -205,7 +205,30 @@ AWARE2.0 **Citation**: Seitfudem, G., Berger, M., Mueller Schmied, H., & Boulay, A.-M. (2025). *The updated and improved method for water scarcity impact assessment in LCA, AWARE2.0*. https://doi.org/10.5281/zenodo.15133241 -**Retrieval**: ``download_aware2_basins`` fetches the geopackage from Zenodo. Basins whose annual agricultural CF is missing (about a fifth of polygons, mostly ice, desert and small islands) are filled with the global median. +**Retrieval**: ``download_aware2`` fetches the geopackage plus two workbooks (intermediate hydrological variables and native characterisation factors with sectoral demand) from Zenodo. Basins whose annual agricultural CF is missing (about a fifth of polygons, mostly ice, desert and small islands) are filled with the global median. + +**Usage**: the basin geometries drive basin-aware region clustering (see :doc:`land_use`); the characterisation factors and hydrological variables build the regional water-scarcity supply curve (see :doc:`water`). + +WaterGAP 2.2e +~~~~~~~~~~~~~ + +**Provider**: Mueller Schmied et al. (2024), via ISIMIP3a + +**Description**: Global hydrological and water-use model output. The model uses the groundwater storage compartment (``groundwstor``; its long-term decline is groundwater depletion), and potential irrigation water consumption in total (``pirruse``) and from groundwater (``pirrusegw``). The surface part (``pirruse - pirrusegw``) is the irrigation surface availability that scales the AWARE scarcity curve; ``pirrusegw`` gives the renewable groundwater band; ``pirruse`` anchors the consumptive-efficiency calibration and the mining ceiling. + +**Coverage**: + * Spatial: Global, 0.5 degree + * Temporal: Monthly, 1901-2019 (obsclim / histsoc, gswp3-w5e5 forcing) + +**Access**: https://files.isimip.org (ISIMIP3a water_global / WaterGAP2-2e); the static continental-area grid via https://doi.org/10.25716/GUDE.0TNY-KJPG. + +**License**: Creative Commons Attribution 4.0 International (CC BY 4.0) + +**Citation**: Mueller Schmied, H., et al. (2024). *The global water resources and use model WaterGAP v2.2e*. Geoscientific Model Development 17, 8817-8852. https://doi.org/10.5194/gmd-17-8817-2024 + +**Retrieval**: ``download_watergap_isimip`` and ``download_watergap_continental_area``. + +**Usage**: The volume basis for the whole water system -- supply envelope, groundwater bands, demand timing and the ``eta_c`` anchor. See :doc:`water`. MIRCA-OS v2 ~~~~~~~~~~~ @@ -1058,7 +1081,7 @@ Huang et al. -- Gridded Irrigation Water Withdrawals **Citation**: Huang, Z., Hejazi, M., Li, X., Tang, Q., Vernon, C., Leng, G., Liu, Y., Doll, P., Eisner, S., Gerten, D., Hanasaki, N., and Wada, Y. (2018). Reconstruction of global gridded monthly sectoral water withdrawals for 1971-2010 and analysis of their spatiotemporal patterns. *Hydrology and Earth System Sciences*, 22, 2117-2133. https://doi.org/10.5194/hess-22-2117-2018 -**Retrieval**: Retrieved via the ``download_huang_irrigation_water`` rule (Zenodo download, 7z extraction). The ``process_huang_irrigation_water`` rule aggregates gridded monthly data to model regions by area-weighted summation. Outputs: ``processing/{name}/water/current_use/monthly_region_water.csv`` and ``processing/{name}/water/current_use/region_growing_season_water.csv``, selected when ``config['water']['supply_scenario']`` is ``"current_use"``. +**Retrieval**: Retrieved via the ``download_huang_irrigation_water`` rule (Zenodo download, 7z extraction). The ``process_huang_irrigation_water`` rule aggregates gridded monthly data to model regions by area-weighted summation. Outputs: ``processing/{name}/water/current_use/monthly_region_water.csv`` and ``processing/{name}/water/current_use/region_growing_season_water.csv``, selected when ``config['water']['data']['availability']`` is ``"current_use"``. **Usage**: Aggregated to regions for validation of water module against observed irrigation withdrawals. diff --git a/docs/index.rst b/docs/index.rst index 78d06428..8b874900 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -94,6 +94,7 @@ Dietary constraints ensure that each country's population meets nutritional requ land_use crop_production + water livestock food_processing nutrition diff --git a/docs/validation.rst b/docs/validation.rst index ba5ab891..3dcc03ad 100644 --- a/docs/validation.rst +++ b/docs/validation.rst @@ -66,7 +66,7 @@ These settings collectively remove the optimizer's degrees of freedom: adjustment to grassland feed yields to compensate for known data gaps. Additional settings select present-day water availability -(``water.supply_scenario: current_use``) and disable health impacts, since the +(``water.data.availability: current_use``) and disable health impacts, since the goal is physical mass balance rather than optimization. diff --git a/docs/water.rst b/docs/water.rst new file mode 100644 index 00000000..48a4cd69 --- /dev/null +++ b/docs/water.rst @@ -0,0 +1,441 @@ +.. SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +.. +.. SPDX-License-Identifier: CC-BY-4.0 + +Water resources +=============== + +Irrigation water is a primary resource in GLADE, tracked from a regional supply +through to the beneficial evapotranspiration that irrigated crops require. This +chapter is the canonical reference for the water representation: the supply +chain, the three water quantities and which constraint each sits on, the source +bands (surface, renewable groundwater, non-renewable groundwater), the scarcity +and depletion accounting, and the consumption-basis efficiency link. Rainfed +("green water") production carries no water constraint -- only blue-water +consumption is characterised. + +Supply chain +------------ + +Water flows from a single free global source, through a tiered regional supply +that carries the scarcity and groundwater signals, into a per-region +*consumption* pool, and finally through an efficiency delivery link to a *field* +bus that irrigated crops draw from: + +.. code-block:: text + + supply:water_source + --(tiered supply: CF -> scarcity, groundwater-band routing)--> + water:{region} <- consumption pool (C) + --(irrigate:{region}, efficiency = eta_c)--> + water_field:{region} <- beneficial/applied water (E) + <--(crop production link, efficiency2 = -E)-- land + +The tiered supply and the pool are on a **consumption** basis; the crop link +consumes the crop's net irrigation requirement (beneficial evapotranspiration). +The delivery link bridges the two -- see `The three water quantities`_. + +The three water quantities +-------------------------- + +Three distinct volumes describe irrigation, and conflating them is the most +common source of error. GLADE keeps them separate: + +.. csv-table:: + :header: Symbol, Quantity, Basis, Data source, Global + + :math:`E`, "Beneficial ET = net irrigation requirement", "crop demand", "GAEZ ``RES05-WDC``", "~596 km3/yr" + :math:`C`, "Consumption (pool / scarcity / depletion)", "supply + impacts", "WaterGAP ``pirruse``", "~1223 km3/yr" + :math:`W`, "Withdrawal (reported only)", "reporting", "Huang (corrected)", "~2334 km3/yr" + +* :math:`E` is what the crop physically needs -- the water that leaves the field + as beneficial transpiration. It is the coefficient on the crop-production + link's water leg. +* :math:`C` is what the basin actually loses to agriculture, including + non-beneficial consumption (canal and soil evaporation that leaves the basin + as vapour). The regional pool, the AWARE scarcity characterisation, and the + groundwater bands are all sized on :math:`C`. WaterGAP's ``pirruse`` (total + irrigation consumption) is a consumption quantity and is source-agnostic (it + already includes groundwater). +* :math:`W` is the volume physically pumped or diverted (Huang et al. 2018 + [huang2018]_, corrected). The difference :math:`W - C` is **return flow** -- + water that runs off or percolates back and is reused downstream. GLADE never + withdraws it, so it is not modelled explicitly; :math:`W` is reported for + comparison only (see `Return flow`_). + +The two efficiencies relating them are the consumptive efficiency +:math:`\eta_c = E / C \approx 0.49` and the consumed fraction +:math:`C / W \approx 0.58`. + +The efficiency delivery link +---------------------------- + +Because the pool is on consumption :math:`C` while the crop needs :math:`E`, a +per-region delivery link ``irrigate:{region}`` sits between them with efficiency +:math:`\eta_c`: + +.. math:: + + \text{(pool draw)}\ C = \frac{E}{\eta_c}, \qquad + \text{(delivered)}\ E = \eta_c \cdot C . + +Per unit :math:`E` delivered to the field bus, the link draws +:math:`C = E / \eta_c` from the pool. For :math:`\eta_c < 1` the difference is +non-beneficial consumption and simply vanishes (it is genuinely lost to the +atmosphere, not returned); for :math:`\eta_c > 1` the region deficit-irrigates +(see below) and one unit of consumption covers more than one unit of nominal +requirement. This puts every supply-side quantity on the correct consumption +basis while keeping the crop coefficient at its physical net requirement. + +:math:`\eta_c` is calibrated per region at build time so the baseline +reproduces observed consumption: + +.. math:: + + \eta_c(r) = \operatorname{clip}\!\left( + \frac{E_\text{baseline}(r)}{C_\text{anchor}(r)},\ \eta_\text{min},\ \eta_\text{max} + \right), + \qquad + \eta_c(r) \ge \frac{E_\text{baseline}(r)}{\text{pool}(r)} , + +where :math:`E_\text{baseline}(r)` is the model's own baseline irrigated area +times net requirement (summed over the region's crop-production links, single +and multi-cropping) and :math:`C_\text{anchor}(r)` is the observed irrigation +consumption (``region_agri_consumption.csv``): WaterGAP's total irrigation +consumption ``pirruse``, the same simulation, basis and reference window as +the supply envelope, so the calibrated baseline draw is consistent with the +supply split by construction. The clip to +:math:`\eta_\text{min}` guards against mistaking a data mismatch for real +inefficiency. Values **above 1 encode deficit irrigation**: in regions where +the GAEZ full requirement exceeds observed consumption (India, Pakistan, +Thailand, Sudan), real irrigation delivers less than the yield-maximising +requirement, so the delivery link stretches each unit of pool consumption +across :math:`\eta_c > 1` units of nominal requirement -- the baseline then +draws the observed consumption instead of the (unobserved) full requirement, +which would otherwise be forced into groundwater mining. +:math:`\eta_\text{max}` bounds the ratio where the consumption anchor is +unreliably small and marginal irrigation would become nearly free. Where +:math:`\eta_c > 1` each unit of pool consumption still satisfies several units +of nominal requirement, so under water pricing marginal irrigation in +deficit-irrigated regions is comparatively cheap; results that hinge on +irrigation expanding there warrant a sensitivity check on ``eta_max``. +Finally, +the floor at :math:`E_\text{baseline} / \text{pool}` guarantees the calibrated +baseline draw never exceeds the region's pool (in overexploited basins the pool +is clipped below observed consumption). Regions where the clip or floor binds +are logged as data-quality diagnostics -- they are the overexploited basins. + +The :math:`\eta_\text{min}` clip has a known blind spot in the opposite +direction from deficit irrigation: **humid paddy regions** -- southern China +above all by volume, with Korea, Japan and parts of Southeast Asia -- where +the GAEZ *net* requirement of irrigated rice is near zero (rainfall covers crop +ET) while WaterGAP's paddy consumption includes ponding and percolation losses. +With :math:`E_\text{baseline} \approx 0` the baseline draws (almost) no water +regardless of :math:`\eta_c`, so the observed consumption (and its small +groundwater mining) cannot emerge there. Similarly, regions clipped at +:math:`\eta_\text{min}` draw below their consumption anchor; Spain is the +notable case where this happens in a genuinely scarce basin. Both are +requirement-basis mismatches between GAEZ and WaterGAP, accepted as residual +error rather than patched with region-specific factors. The missed volume is +a noticeable share of global irrigation consumption, but since it sits mostly +in humid, low-CF basins it is a much smaller share of baseline *scarcity* +(Spain being the exception). The flip side is conservative: water savings +from paddy water management are outside the model's reach, so +scarcity-reduction results cannot overclaim them. + +Surface availability (WaterGAP envelope) +---------------------------------------- + +AWARE's availability is basin *river discharge*, which misstates the surface +water accessible to irrigation in two ways. Its **volume** counts through-flow +discharge as divertible: in the Texas High Plains (Ogallala), AWARE reports a +pool ~100 times the surface water WaterGAP's detailed allocation supplies, so +the model draws free "surface" water where irrigation in reality mines a fossil +aquifer. Its **timing** is unregulated discharge seasonality: rivers peak with +the monsoon or snowmelt, while real delivery is shifted into the irrigation +season by reservoirs -- WaterGAP's ``histsoc`` runs operate every GRanD +reservoir >= 0.5 km3 (Hanasaki scheme), so the monthly profile of its irrigation +surface consumption is regulated, demand-timed delivery. Keeping AWARE's +discharge timing strands that delivery in the wet months and overstates +dry-season mining (globally ~265 km3/yr). + +GLADE therefore keeps AWARE's scarcity structure -- the per-basin CF curve -- +but sets surface volume and timing from WaterGAP's monthly climatological +irrigation surface consumption (:math:`\text{pirruse} - \text{pirrusegw}`, +ISIMIP3a WaterGAP 2.2e). The builder overlays that 0.5-degree WaterGAP field +directly with every (model-region, AWARE-basin) intersection. Each regional +total is conserved exactly, but its within-region basin split follows +WaterGAP's grid-cell delivery rather than AWARE basin area. Where WaterGAP +reports little surface (the Ogallala), the surface tiers shrink toward zero and +the residual demand draws groundwater; where irrigation is genuinely surface-fed +(California's Central Valley), the pool is largely retained but re-timed into +the irrigation season. + +AWARE basin-months with no agricultural pool are already over-allocated: their +AMD is non-positive after irrigation is restored. WaterGAP delivery mapped to +such a cell remains available, but receives AWARE's maximum CF of 100 instead +of being reassigned to an unrelated lower-scarcity basin. A rare regional +WaterGAP residual with no AWARE-basin intersection is likewise retained on an +explicit CF-100 tier. The WaterGAP surface field is built by +``build_region_watergap.py``; its basin overlay and AWARE tier construction are +applied in ``build_region_water_aware.py``. + +The division of labour between the two datasets is deliberate: **WaterGAP +defines every volume** -- the surface envelope, the groundwater bands, the +irrigation-consumption anchor for :math:`\eta_c` and the mining ceiling -- all +from one simulation (ISIMIP3a ``histsoc``) and one basis (consumption). Surface +delivery and its consumption anchor use the AWARE-aligned 1990-2019 reference; +the storage-decline depletion trend uses 2000-2019. **AWARE contributes only +the scarcity valuation** (the CF curve, a function of ``amd0``) and its native +basin geometry. Mixing volume sources would reintroduce cross-dataset +inconsistencies between what the baseline draws and what the envelope supplies. + +Limits of the hybrid metric +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The WaterGAP surface envelope is the modelled potential irrigation consumption +allocated to surface water in the historical ``histsoc`` run. It is the best +available representation here of regulated, crop-timed delivery, but it is not +an endogenous natural-water-resource curve. The model therefore asks how the +food system can reorganize within the observed WaterGAP delivery pattern, not +how it would redesign reservoirs, canals or inter-basin transfers. + +The default WaterGAP delivery window now matches AWARE2.0's 1990-2019 scarcity +reference; the groundwater-storage depletion trend remains deliberately recent +(2000-2019). Replacing AWARE capacity with WaterGAP delivery improves physical +allocation but does not recalculate AWARE's hydrology or re-anchor the CF curve +to that delivery. In addition, a native basin that crosses model regions has one +independent curve per region; the LP does not couple simultaneous drawdown across +those regions. An exact treatment would make the native basin, rather than the +model region, the shared water-supply node and would be a separate structural +model change. + +Temporal resolution (intra-year periods) +---------------------------------------- + +Physical basin availability is not the surface water a crop can actually use: +monsoon-month runoff cannot serve a dry-season crop without storage. Summing a +year's availability lets wet-season surplus subsidise the dry season and erases +the temporal mismatch that drives real groundwater mining. Rather than baking a +seasonal cap into a scalar, the model resolves supply and demand at +``water.temporal_resolution`` (a structural divisor of 12): the year is split +into :math:`T` equal periods and each is balanced in the LP. + +- ``build_region_water_aware.py`` emits the convex scarcity curve per region + *and month*; ``compose_water_supply.py`` groups whole months into the + :math:`T` periods (month :math:`m \to \lfloor (m-1) T / 12 \rfloor`) and + re-merges the monthly curves into one convex curve per region-period. +- Each region-period gets its own water bus ``water:{region}:p{p}``; the tier + capacities cap that period's surface draw. +- Every irrigated crop's net requirement is split across the periods by the + observed crop calendar (see below), so a monsoon crop competes for wet-season + water and a winter crop for dry-season water on the + ``water_field:{region}:p{p}`` buses. + +A period whose surface cannot meet the demand landing in it draws groundwater +(mining) endogenously; period surplus goes undispatched (there is no +inter-period surface storage link -- reservoir regulation is instead imported +exogenously through WaterGAP's monthly delivery profile, see above). +:math:`T=1` recovers the annual model (no seasonal binding); :math:`T=12` is the +faithful monthly model; :math:`T=4` (quarterly) captures wet/dry seasonality at +a fraction of the solve cost. Cost scales ~linearly in :math:`T` on the water +side of the model. + +**The shipped default is** :math:`T=1`, which is cheap and adequate wherever +water is not the object of study -- but be clear about what it buys. At +:math:`T=1` a region's whole annual pool is available to every season, which is +exactly the wet-season-subsidises-dry-season averaging this design exists to +remove. The practical consequence is that **the groundwater bands go nearly +inert**: surface alone covers demand almost everywhere, so reported depletion +falls to near zero. That near-zero is an artefact of the resolution, not a +finding. Any study about water, irrigation or groundwater should raise +:math:`T` (4 is a reasonable compromise). + +.. note:: + + Above :math:`T=4` a crop-production link crosses ten ports (:math:`T=6` + reaches ``bus11``, :math:`T=12` reaches ``bus17``). PyPSA resolves numeric + ``at_port`` labels positionally against a lexicographically sorted port list, + so at ten or more ports those labels silently select the wrong buses. Filter + statistics by ``bus_carrier``, never by numeric ``at_port``. + +The accumulated scarcity total itself grows with :math:`T`: finer resolution +exposes dry-season draws to the high monthly CFs that annual averaging smooths +away. Absolute scarcity levels are therefore only comparable between runs at +the same temporal resolution; cross-scenario comparisons should hold :math:`T` +fixed and lean on relative changes. + +Demand calendar (MIRCA-OS, retimed to WaterGAP) +----------------------------------------------- + +Placing demand *when* it actually occurs matters as much as placing supply. The +GAEZ growing seasons are the yield-maximising potential calendar, which +systematically disagrees with observed cropping calendars in the major irrigated +systems (the Indus, the Nile, the Gangetic plain) -- so GAEZ-timed demand lands +in months WaterGAP does not deliver surface water and is covered by groundwater +mining instead. GLADE therefore places irrigation demand by the **observed** +calendar: ``build_mirca_crop_calendar`` aggregates the MIRCA-OS 2015 monthly +irrigated growing-area grids to per-(region, crop) monthly demand shares. A +calendar-only supplement mapping (``mirca_os_calendar_supplement.csv``) adds the +MIRCA classes excluded from the multi-cropping concordance (sugar cane, pulses, +fodder) so those large irrigators are also placed by observed timing. + +Growing-*area* months are still not requirement months: within a season the net +irrigation requirement follows evapotranspiration minus effective precipitation +-- it collapses during the monsoon and peaks in the dry shoulder months -- +while the area profile weights every growing month equally (including dormant +winter-wheat months). The shares are therefore **retimed by iterative +proportional fitting** to WaterGAP's monthly irrigation requirement +(``pirruse``, the same simulation and basis as the supply envelope): per region, +the crop x month prior (area shares weighted by each crop's annual irrigation +water) is scaled so that region-month totals match the WaterGAP monthly shape +while each crop's annual total and the structural zeros of its observed season +are preserved exactly -- wheat shifts within its rabi window but never into the +monsoon. + +Both the single-crop links (``build_model.crops``) and the multi-cropping +cycles (``build_multi_cropping``) bin the retimed shares into the :math:`T` +periods. Where MIRCA has no observation for a (region, crop) the GAEZ growing +season is the fallback. The 2015 vintage is used deliberately -- the 2020 +MIRCA-OS calendar misplaces the northwest-India wheat belt into the monsoon +window (see :doc:`data_sources`). + +Source bands +------------ + +With ``water.supply.groundwater`` two groundwater ``source`` bands expand the +supply envelope so that mining emerges endogenously wherever surface falls short +of demand (it is no longer capped at renewable availability). Unlike surface, +which is period-bound, **groundwater is an annual per-region resource**: an +aquifer integrates recharge over the year and can be pumped in any period. Each +region therefore gets a ``groundwater:{region}`` bus, fed by the two annual bands +and distributed to every period's water bus by free delivery links, so a dry +period can draw the whole year's recharge: + +.. csv-table:: + :header: source, Meaning, Sizing (annual, per region), Scarcity / impact + + ``renewable`` (surface), "Surface + recharged blue water", "That period's convex surface curve (period-bound)", "AWARE CF -> ``impact:water_scarcity``" + ``groundwater_renewable``, "Recharged groundwater abstraction", ":math:`\max(\text{pirrusegw} - \text{mined},\ 0)`", "AWARE CF + tally on ``impact:groundwater_renewable``" + ``groundwater_nonrenewable``, "Mined (depleting) groundwater", ":math:`\text{ceiling\_factor} \times C` (generous, non-binding)", "Mined volume -> ``impact:groundwater_depletion``" + +``compose_water_supply.py`` writes the surface tiers (per region-period) to +``region_water_tiers.csv`` and the annual groundwater bands (per region) to +``region_groundwater_bands.csv``. The renewable band is the WaterGAP volume, +priced at the region's scarcest surface CF so it is drawn after surface but +before mining. The non-renewable band's capacity is a deliberately generous +ceiling (``water.supply.groundwater_ceiling_factor`` times annual consumption): +the volume actually mined is set endogenously by how far surface plus renewable +groundwater fall short of demand -- the pumping cost keeps the draw minimal, so +the ceiling itself does not bind. The groundwater sizing fields come from +WaterGAP 2.2e via ``build_region_watergap.py`` (see :doc:`data_sources`): mining +is the groundwater-storage decline and renewable groundwater is the recharged +part of irrigation groundwater consumption (:math:`\text{pirrusegw}`). There is +no endogenous inter-period surface storage; current reservoir operation enters +through the WaterGAP monthly surface profile, so mining reflects the deficit +under today's regulation. With ``water.supply.groundwater: false`` there are no +groundwater bands and the supply is surface only. + +Scarcity accounting +------------------- + +The AWARE characterisation factor (CF, m3 world-equivalent per m3 consumed) +measures how scarce a basin's water is. Both CF-carrying bands (``renewable`` +and ``groundwater_renewable``) accumulate their drawn volume times the tier CF +onto the global ``impact:water_scarcity`` store: + +.. math:: + + \text{scarcity} = \sum_{\text{CF tiers } t} \mathrm{CF}_t \cdot \text{draw}_t + \quad [\text{Mm}^3\ \text{world-eq}]. + +The convex, demand-dependent CF curve is reconstructed from AWARE's marginal CF +in ``build_region_water_aware.py`` (as the model draws down a basin's pool its +AMD falls and the CF rises), discretised into tiers, and drawn low-CF-first via +a negligible merit-order regularizer. At solve time the accumulated scarcity can +be priced (``water_scarcity.price``) or capped +(``water_scarcity.cap_mm3_world_eq``). + +Groundwater depletion accounting +-------------------------------- + +Non-renewable groundwater (``groundwater_nonrenewable``) does not carry a CF; +instead each unit drawn accumulates 1:1 on the ``impact:groundwater_depletion`` +store (Mm3 mined), and the band carries a small real pumping cost +(``water.supply.pumping_cost_usd_per_m3``) that both adds realism and orders it +last in the merit order (drawn only once a region's renewable water is +exhausted). At solve time depletion can be priced +(``groundwater_depletion.price``) or capped (``groundwater_depletion.cap_mm3``, +e.g. down to zero to ask how the food system reorganizes without mining). + +AWARE covers renewable water only and excludes fossil stocks, so under +scarcity pricing alone the CF-free mined band would become the cheapest source +wherever the scarcity charge exceeds the pumping cost, and "relief" would be +substitution into fossil groundwater rather than conservation. +``water_scarcity.nonrenewable_cf`` therefore charges each mined m3 at +``nonrenewable_cf * water_scarcity.price``. The default 30 is the +mining-volume-weighted mean local AWARE CF of the regions that actually +deplete groundwater (so a mined m3 is priced at the scarcity of the exhausted +renewable water it displaces); weights below that reintroduce the substitution +at low-to-mid prices, while 100 (AWARE's demand-exceeds-availability cutoff +plus a non-renewability premium) is the precautionary upper anchor. Set it to +``null`` to study depletion as a separate axis via the ``groundwater_depletion`` +options (enabling both pricings together is an error). + +The renewable-groundwater band additionally tallies its drawn volume on +``impact:groundwater_renewable`` (via a ``bus3`` output) purely for reporting +and as a hook for future policy; it does not affect the baseline solve. + +Return flow +----------- + +Return flow -- withdrawal minus consumption, reused downstream in reality -- is +handled **implicitly**. On a consumption basis it is simply never withdrawn: the +consumption pool already excludes it, and the model only ever draws the +consumption :math:`C`. Modelling it explicitly would only be necessary for +withdrawal-based accounting or explicit upstream-downstream reuse (a possible +future extension with basin topology). The consumption basis also keeps a future +drip-irrigation feature honest -- efficiency gains are credited only for +reducing consumption, not for reducing withdrawal that was returning anyway (the +irrigation-efficiency paradox). + +Irrigation efficiency and technology +------------------------------------ + +The single ``irrigate:{region}`` delivery link generalises to parallel +per-technology links (flood, drip) with their own efficiencies and capital +costs, letting the model invest in more efficient irrigation to draw less +:math:`C` per unit :math:`E`. That technology-investment feature is a planned +extension; the current single-link formulation is the seam it slots into without +reworking the pool, scarcity, or bands. + +Model components +---------------- + +.. csv-table:: + :header: Component, Name, Carrier, Role + + Bus, ``water:source``, ``water_source``, "Free global water source" + Bus, ``water:{region}:p{p}``, ``water``, "Regional consumption pool (per period)" + Bus, ``water_field:{region}:p{p}``, ``water_field``, "Beneficial/applied water for crops (per period)" + Bus, ``groundwater:{region}``, ``groundwater``, "Annual per-region aquifer pool (when supply.groundwater)" + Bus, ``impact:water_scarcity``, ``water_scarcity``, "Accumulated AWARE scarcity" + Bus, ``impact:groundwater_depletion``, ``groundwater_depletion``, "Accumulated mined volume" + Bus, ``impact:groundwater_renewable``, ``groundwater_renewable``, "Renewable-GW volume tally" + Link, ``supply:water:{region}:p{p}:t{n}``, ``water_supply``, "Tiered surface supply (source, CF)" + Link, ``supply:groundwater:{region}:{source}``, ``water_supply``, "Annual groundwater bands (when supply.groundwater)" + Link, ``deliver:groundwater:{region}:p{p}``, ``groundwater_delivery``, "Annual aquifer -> period pool (free)" + Link, ``irrigate:{region}:p{p}``, ``irrigation_delivery``, "Consumption -> field (eta_c)" + Store, ``store:impact:water_scarcity``, ``water_scarcity``, "Priced/capped at solve time" + Store, ``store:impact:groundwater_depletion``, ``groundwater_depletion``, "Priced/capped at solve time" + Store, ``store:impact:groundwater_renewable``, ``groundwater_renewable``, "Reporting only" + +Units: water volumes are Mm3 (10^6 m3); scarcity is Mm3 world-equivalent; +depletion is Mm3 mined. See :doc:`configuration` for the ``water`` config block +and the solve-time levers, :doc:`workflow` for the build rules, and +:doc:`analysis` for the ``water_metrics`` outputs. + +References +---------- + +.. [huang2018] Huang, Z., Hejazi, M., Li, X., Tang, Q., Vernon, C., Leng, G., Liu, Y., Doll, P., Eisner, S., Gerten, D., Hanasaki, N., and Wada, Y. (2018). Reconstruction of global gridded monthly sectoral water withdrawals for 1971-2010 and analysis of their spatiotemporal patterns. *Hydrology and Earth System Sciences*, 22, 2117-2133. https://doi.org/10.5194/hess-22-2117-2018 diff --git a/docs/workflow.rst b/docs/workflow.rst index 27a433cf..7bb4cac3 100644 --- a/docs/workflow.rst +++ b/docs/workflow.rst @@ -115,22 +115,34 @@ Data Preparation Rules * **Script**: ``workflow/scripts/process_blue_water_availability.py`` * **Purpose**: Build monthly basin-level blue water availability -**build_region_water_sustainable** - * **Input**: Blue water availability, regions, crop yields - * **Output**: ``processing/{name}/water/sustainable/monthly_region_water.csv``, ``processing/{name}/water/sustainable/region_growing_season_water.csv`` - * **Script**: ``workflow/scripts/build_region_water_availability.py`` - * **Purpose**: Allocate basin availability to regions and growing seasons +**build_region_watergap** + * **Input**: WaterGAP 2.2e (ISIMIP3a) groundwater storage, potential irrigation consumption total and from groundwater, continental area, regions + * **Output**: ``processing/{name}/water/watergap/`` -- ``region_watergap_surface.csv`` (monthly irrigation surface consumption), ``region_groundwater_depletion.csv``, ``region_agri_consumption.csv`` (the eta_c and mining-ceiling anchor), ``region_watergap_demand.csv`` (monthly demand, the calendar retiming target) + * **Script**: ``workflow/scripts/build_region_watergap.py`` + * **Purpose**: Aggregate WaterGAP surface availability, the groundwater bands and the irrigation-consumption anchor per region + +**build_region_water_aware** + * **Input**: AWARE2.0 intermediate variables, native CFs, basin polygons, regions, crop yields, WaterGAP surface + * **Output**: ``processing/{name}/water/aware/`` -- monthly and growing-season availability plus ``region_water_tiers.csv`` + * **Script**: ``workflow/scripts/build_region_water_aware.py`` + * **Purpose**: Build the convex water-scarcity supply curve; AWARE contributes the CF curve and basin distribution, WaterGAP the volumes and timing **build_region_water_current_use** - * **Input**: Huang irrigation withdrawals, regions, crop yields - * **Output**: ``processing/{name}/water/current_use/monthly_region_water.csv``, ``processing/{name}/water/current_use/region_growing_season_water.csv`` - * **Script**: ``workflow/scripts/process_huang_irrigation_water.py`` - * **Purpose**: Aggregate current irrigation withdrawals to regions and growing seasons - -**select_water_scenario** - * **Input**: Scenario-specific water outputs - * **Output**: ``processing/{name}/water/monthly_region_water.csv``, ``processing/{name}/water/region_growing_season_water.csv`` - * **Purpose**: Copy the configured water supply scenario into the unified paths used by the model + * **Input**: Huang et al. gridded irrigation withdrawals, regions, crop yields + * **Output**: ``processing/{name}/water/current_use/`` (same schema) + * **Purpose**: Present-day withdrawal alternative, selected by ``water.data.availability`` + +**build_mirca_crop_calendar** + * **Input**: MIRCA-OS 2015 monthly irrigated growing-area grids, crop concordance and calendar supplement, WaterGAP monthly demand, crop yields, regions + * **Output**: ``processing/{name}/water/mirca_crop_calendar.csv`` + * **Script**: ``workflow/scripts/build_mirca_crop_calendar.py`` + * **Purpose**: Observed per-(region, crop) monthly irrigation demand shares, retimed by iterative proportional fitting so region-month totals follow WaterGAP while each crop's annual total and observed season are preserved + +**compose_water_supply** + * **Input**: The selected availability source's outputs, plus the WaterGAP groundwater bands when ``water.supply.groundwater`` + * **Output**: ``processing/{name}/water/`` -- ``region_water_tiers.csv`` (per-period surface), ``region_groundwater_bands.csv`` (annual per-region groundwater), and the availability tables copied through + * **Script**: ``workflow/scripts/compose_water_supply.py`` + * **Purpose**: Group months into the model's intra-year periods and finish the supply tables: keep the convex scarcity curve or collapse it to a flat cap, and emit the groundwater bands **build_current_grassland_area** * **Input**: Resource classes, land-cover fractions (``processing/{name}/luc/lc_masks.nc``), regions diff --git a/tests/config/test.yaml b/tests/config/test.yaml index 3dbf943a..5b4f5996 100644 --- a/tests/config/test.yaml +++ b/tests/config/test.yaml @@ -64,3 +64,12 @@ crops: # cropgrids_crops <-> crops invariant is obvious in the test config. cropgrids_crops: - apple + +# Exercise the full-fidelity water path in CI: the shipped default is annual +# with collapsed tiers (cheap), so without this nothing would cover the +# seasonal split, the convex scarcity curve or the groundwater bands. +water: + temporal_resolution: 4 + supply: + scarcity_tiers: true + groundwater: true diff --git a/tests/test_crop_calendar_retiming.py b/tests/test_crop_calendar_retiming.py new file mode 100644 index 00000000..350d34cc --- /dev/null +++ b/tests/test_crop_calendar_retiming.py @@ -0,0 +1,171 @@ +""" +SPDX-FileCopyrightText: 2026 Koen van Greevenbroek + +SPDX-License-Identifier: GPL-3.0-or-later + +Unit tests for the IPF retiming of MIRCA crop-calendar shares to WaterGAP's +monthly irrigation requirement (build_mirca_crop_calendar). +""" + +import numpy as np +import pandas as pd +import pytest + +from workflow.scripts.build_mirca_crop_calendar import retime_shares_to_demand + +MONTHS = list(range(1, 13)) + + +def _shares(region, crop, profile): + profile = np.asarray(profile, dtype=float) + return pd.DataFrame( + { + "region": region, + "crop": crop, + "month": MONTHS, + "share": profile / profile.sum(), + "area_ha": profile * 100.0, + } + ) + + +def _demand(region, profile): + return pd.DataFrame( + { + "region": region, + "month": MONTHS, + "irrigation_consumption_mm3": np.asarray(profile, dtype=float), + } + ) + + +def test_retiming_matches_demand_shape_and_preserves_rows(): + # Two crops with overlapping flat seasons; demand concentrated late. + shares = pd.concat( + [ + _shares("r1", "wheat", [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1]), + _shares("r1", "rice", [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]), + ], + ignore_index=True, + ) + weights = pd.Series({("r1", "wheat"): 60.0, ("r1", "rice"): 60.0}).rename_axis( + ["region", "crop"] + ) + # A feasible target: the column sums of an explicit crop x month matrix + # respecting both seasons (wheat 10 in each of its 6 months; rice + # 5/10/5/5/5/15/15 over months 4-10), so IPF can converge exactly. + demand = _demand("r1", [10, 10, 10, 15, 10, 5, 5, 5, 15, 15, 10, 10]) + + out = retime_shares_to_demand(shares, weights, demand) + + # Per-crop annual totals preserved exactly: shares sum to 1. + sums = out.groupby(["region", "crop"])["share"].sum() + assert np.allclose(sums, 1.0) + + # Region-month totals (share x weight) match the demand shape. + wide = out.pivot_table(index="crop", columns="month", values="share") + total = (wide.loc["wheat"] * 60.0 + wide.loc["rice"] * 60.0).to_numpy() + target = np.array([10, 10, 10, 15, 10, 5, 5, 5, 15, 15, 10, 10], dtype=float) + assert np.allclose(total, target, atol=1e-3 * 120.0) + + +def test_structural_zeros_preserved(): + shares = _shares("r1", "wheat", [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1]) + weights = pd.Series({("r1", "wheat"): 10.0}).rename_axis(["region", "crop"]) + # Demand peaks in the monsoon months where wheat does not grow. + demand = _demand("r1", [1, 1, 1, 1, 5, 20, 30, 20, 5, 1, 1, 1]) + + out = retime_shares_to_demand(shares, weights, demand) + monsoon = out[(out["month"] >= 4) & (out["month"] <= 10)] + assert (monsoon["share"] == 0.0).all() + assert np.isclose(out["share"].sum(), 1.0) + + +def test_zero_weight_and_missing_demand_pass_through(): + shares = pd.concat( + [ + _shares("r1", "wheat", [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]), + _shares("r2", "wheat", [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]), + ], + ignore_index=True, + ) + # r1 has no weight; r2 has weight but no demand row at all. + weights = pd.Series({("r1", "wheat"): 0.0, ("r2", "wheat"): 5.0}).rename_axis( + ["region", "crop"] + ) + demand = _demand("r1", [0.0] * 12) # also below min_demand_mm3 + + out = retime_shares_to_demand(shares, weights, demand) + pd.testing.assert_frame_equal( + out.reset_index(drop=True), shares.reset_index(drop=True) + ) + + +def test_unservable_demand_months_left_unmet_but_rows_normalised(): + # Demand exists in months no crop grows: IPF cannot serve them; the + # profile must still normalise to 1 within the crop's season. + shares = _shares("r1", "wheat", [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) + weights = pd.Series({("r1", "wheat"): 10.0}).rename_axis(["region", "crop"]) + demand = _demand("r1", [1, 1, 0, 0, 0, 30, 0, 0, 0, 0, 0, 0]) + + out = retime_shares_to_demand(shares, weights, demand) + assert np.isclose(out["share"].sum(), 1.0) + assert out.loc[out["month"] == 6, "share"].item() == 0.0 + # The two feasible months split evenly (equal demand, equal prior). + jan_feb = out.loc[out["month"].isin([1, 2]), "share"] + assert np.allclose(jan_feb, 0.5) + + +def test_multi_region_independence(): + shares = pd.concat( + [ + _shares("r1", "wheat", [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]), + _shares("r2", "wheat", [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0]), + ], + ignore_index=True, + ) + weights = pd.Series({("r1", "wheat"): 10.0, ("r2", "wheat"): 10.0}).rename_axis( + ["region", "crop"] + ) + demand = pd.concat( + [ + _demand("r1", [3, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]), + _demand("r2", [0, 0, 0, 0, 0, 0, 1, 2, 3, 0, 0, 0]), + ], + ignore_index=True, + ) + + out = retime_shares_to_demand(shares, weights, demand) + r1 = out[out["region"] == "r1"].set_index("month")["share"] + r2 = out[out["region"] == "r2"].set_index("month")["share"] + assert np.allclose(r1.loc[[1, 2, 3]], np.array([3, 2, 1]) / 6.0) + assert np.allclose(r2.loc[[7, 8, 9]], np.array([1, 2, 3]) / 6.0) + + +def test_no_overlap_with_demand_keeps_observed_shares(): + # The crop's whole season falls in months with zero demand: it cannot be + # retimed and must keep its observed (MIRCA) profile. + shares = _shares("r1", "wheat", [1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]) + weights = pd.Series({("r1", "wheat"): 10.0}).rename_axis(["region", "crop"]) + demand = _demand("r1", [0, 0, 0, 0, 0, 10, 20, 10, 0, 0, 0, 0]) + + out = retime_shares_to_demand(shares, weights, demand) + pd.testing.assert_frame_equal( + out.reset_index(drop=True), shares.reset_index(drop=True) + ) + + +def test_retiming_shape_error_on_lost_rows(): + # A malformed weights index (unknown region) must not corrupt the output. + shares = _shares("r1", "wheat", [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) + weights = pd.Series({("rX", "wheat"): 1.0}).rename_axis(["region", "crop"]) + demand = _demand("r1", [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) + out = retime_shares_to_demand(shares, weights, demand) + # No active rows -> passthrough. + pd.testing.assert_frame_equal( + out.reset_index(drop=True), shares.reset_index(drop=True) + ) + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/tests/test_crop_costs.py b/tests/test_crop_costs.py index b8f2954b..388edc68 100644 --- a/tests/test_crop_costs.py +++ b/tests/test_crop_costs.py @@ -53,6 +53,10 @@ def test_silage_maize_cost_not_zero_with_zero_harvested_area(): rice_methane_factor=0.0, rainfed_wetland_rice_ch4_scaling_factor=1.0, use_actual_production=False, + water_periods=1, + irrigation_calendar=pd.DataFrame( + columns=["region", "crop", "month", "share", "area_ha"] + ), min_yield_t_per_ha=0.01, seed_kg_dm_per_ha=pd.Series({"silage-maize": 0.0}), crop_loss_multiplier=pd.Series(dtype=float), @@ -106,7 +110,7 @@ def _add_rice_wheat_multi_link( "resource_class": [0], "water_supply": ["r"], "eligible_area_ha": [1_000_000.0], - "water_requirement_m3_per_ha": [0.0], + "water_requirement_m3_per_ha_p0": [0.0], } ) cycle_yields = pd.DataFrame( @@ -143,6 +147,7 @@ def _add_rice_wheat_multi_link( ), global_median_cost=pd.Series({"wetland-rice": 100.0, "wheat": 200.0}), fertilizer_n_rates={"wetland-rice": 0.0, "wheat": 0.0}, + water_periods=1, rice_methane_factor=0.0, rainfed_wetland_rice_ch4_scaling_factor=1.0, min_yield_t_per_ha=0.01, @@ -256,7 +261,7 @@ def test_multi_cropping_rice_emits_methane_per_cycle(): "resource_class": [0], "water_supply": ["i"], "eligible_area_ha": [1_000_000.0], - "water_requirement_m3_per_ha": [1000.0], + "water_requirement_m3_per_ha_p0": [1000.0], } ) cycle_yields = pd.DataFrame( @@ -279,6 +284,7 @@ def test_multi_cropping_rice_emits_methane_per_cycle(): crop_costs=pd.Series({("wetland-rice", "USA"): 100.0}), global_median_cost=pd.Series({"wetland-rice": 100.0}), fertilizer_n_rates={"wetland-rice": 0.0}, + water_periods=1, rice_methane_factor=110.0, rainfed_wetland_rice_ch4_scaling_factor=0.5, min_yield_t_per_ha=0.01, diff --git a/tests/test_integration.py b/tests/test_integration.py index 3e120d85..b48cf1fb 100644 --- a/tests/test_integration.py +++ b/tests/test_integration.py @@ -66,15 +66,33 @@ def test_build_solve_analyze(results_dir): multi = links[links["carrier"] == "crop_production_multi"] assert not multi.empty, "expected crop_production_multi links" assert (multi["baseline_area_mha"] > 0).any(), "multi links lack a baseline anchor" - # Irrigated multi links must carry a water port (no free irrigation). + # Irrigated multi links must carry per-period water ports (no free + # irrigation), and at the test config's temporal_resolution the demand must + # actually be seasonal rather than smeared evenly across the year. bus_cols = [c for c in multi.columns if c.startswith("bus") and c[3:].isdigit()] irrigated = multi[multi["water_supply"] == "irrigated"] - if not irrigated.empty: - has_water = irrigated[bus_cols].apply( - lambda row: any(isinstance(v, str) and v.startswith("water:") for v in row), - axis=1, - ) - assert has_water.all(), "irrigated multi links without a water port" + assert not irrigated.empty, "expected irrigated multi-cropping links" + has_water = irrigated[bus_cols].apply( + lambda row: any( + isinstance(v, str) and v.startswith("water_field:") for v in row + ), + axis=1, + ) + assert has_water.all(), "irrigated multi links without a water_field port" + + found_nonuniform = False + for _, row in irrigated.iterrows(): + effs = [ + abs(float(row[f"efficiency{c[3:]}"])) + for c in bus_cols + if isinstance(row[c], str) + and row[c].startswith("water_field:") + and f"efficiency{c[3:]}" in row + ] + if len(effs) >= 2 and max(effs) > 1.2 * (sum(effs) / len(effs)): + found_nonuniform = True + break + assert found_nonuniform, "no irrigated multi link with a seasonal water split" @pytest.mark.plots diff --git a/tests/test_water_groundwater.py b/tests/test_water_groundwater.py new file mode 100644 index 00000000..5a5abd83 --- /dev/null +++ b/tests/test_water_groundwater.py @@ -0,0 +1,330 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: GPL-3.0-or-later + +"""Unit tests for the non-renewable groundwater supply band. + +Covers the three pure/near-pure pieces of the groundwater feature without a full +Snakemake build: the depletion-trend computation, the tier decomposition +(carve/collapse), the source-split metrics, and the solve-time levers. +""" + +import numpy as np +import pandas as pd +import pypsa +import pytest +import xarray as xr + +from workflow.scripts.analysis.extract_water_metrics import extract_water_totals +from workflow.scripts.build_model.irrigation import calibrate_eta_c +from workflow.scripts.build_model.primary_resources import ( + _retain_material_water_capacities, +) +from workflow.scripts.build_region_watergap import ( + SECONDS_PER_MONTH, + compute_depletion_raster, + compute_monthly_flux_raster, +) +from workflow.scripts.compose_water_supply import ( + build_groundwater_bands, + collapse_single, +) +from workflow.scripts.solve_model.core import ( + add_groundwater_depletion_cap, + add_groundwater_depletion_pricing_to_objective, +) + + +def _renewable_tiers() -> pd.DataFrame: + """Two regions of ascending-CF renewable tiers, one period (the per-period + surface curve build_groundwater_bands / collapse_single consume).""" + return pd.DataFrame( + { + "region": ["r1", "r1", "r1", "r2", "r2"], + "period": [0, 0, 0, 0, 0], + "tier": [0, 1, 2, 0, 1], + "capacity_mm3": [10.0, 20.0, 30.0, 5.0, 15.0], + "marginal_cf": [0.5, 1.0, 5.0, 0.2, 2.0], + } + ) + + +def test_water_supply_omits_sub_cubic_metre_capacities(): + """Roundoff-scale supply bands cannot enter the optimization problem.""" + capacities = pd.DataFrame( + { + "region": ["r1", "r1", "r2"], + "capacity_mm3": [1e-8, 1e-6, 2.0], + } + ) + + retained = _retain_material_water_capacities(capacities, 1e-6) + + assert retained["capacity_mm3"].tolist() == [1e-6, 2.0] + + +# --------------------------------------------------------------------------- # +# Depletion trend +# --------------------------------------------------------------------------- # +def test_compute_depletion_only_from_declining_storage(tmp_path): + """Only cells with a falling storage trend contribute positive depletion.""" + lat = np.array([0.25, -0.25]) + lon = np.array([0.25, 0.75]) + trend_start, trend_end = 2000, 2004 + n_months = (trend_end - 1901 + 1) * 12 + months = np.arange(n_months, dtype=float) + year = 1901 + months // 12 + + data = np.zeros((n_months, 2, 2), dtype=float) + # (0,0) declining 12 mm/yr; (0,1) flat; (1,0) rising; (1,1) flat. + data[:, 0, 0] = 1000.0 - 12.0 * (year - 1901) + data[:, 0, 1] = 500.0 + data[:, 1, 0] = 100.0 + 6.0 * (year - 1901) + data[:, 1, 1] = 500.0 + + ds = xr.Dataset( + {"groundwstor": (("time", "lat", "lon"), data)}, + coords={"time": months, "lat": lat, "lon": lon}, + ) + path = tmp_path / "groundwstor.nc" + ds.to_netcdf(path) + area_path = tmp_path / "continentalarea.nc" + xr.Dataset( + {"continentalarea": (("time", "lat", "lon"), np.ones((1, 2, 2)))}, + coords={"time": [-1900.0], "lat": lat, "lon": lon}, + ).to_netcdf(area_path) + + depletion_m3, _out_lat, _out_lon = compute_depletion_raster( + str(path), str(area_path), trend_start, trend_end + ) + assert depletion_m3.shape == (2, 2) + assert depletion_m3[0, 0] > 0.0 # declining -> depletion + assert depletion_m3[0, 1] == 0.0 # flat + assert depletion_m3[1, 0] == 0.0 # rising -> no depletion + assert depletion_m3[1, 1] == 0.0 + # 12 mm/yr over the specified 1 km2 continental area is 12,000 m3/yr. + assert depletion_m3[0, 0] == pytest.approx(12_000.0) + + +def test_monthly_flux_uses_watergap_continental_area(tmp_path): + """Flux conversion uses WaterGAP continental area, not full grid-cell area.""" + lat = np.array([0.25]) + lon = np.array([0.25]) + flux_path = tmp_path / "pirruse.nc" + xr.Dataset( + {"pirruse": (("time", "lat", "lon"), np.full((12, 1, 1), 0.001))}, + coords={"time": np.arange(12, dtype=float), "lat": lat, "lon": lon}, + ).to_netcdf(flux_path) + area_path = tmp_path / "continentalarea.nc" + xr.Dataset( + {"continentalarea": (("time", "lat", "lon"), np.array([[[2.5]]]))}, + coords={"time": [-1900.0], "lat": lat, "lon": lon}, + ).to_netcdf(area_path) + + monthly, _out_lat, _out_lon = compute_monthly_flux_raster( + str(flux_path), "pirruse", str(area_path), 1901, 1901 + ) + + expected = 0.001 * SECONDS_PER_MONTH * 0.001 * 2.5e6 + assert np.allclose(monthly[:, 0, 0], expected) + + +# --------------------------------------------------------------------------- # +# Tier decomposition +# --------------------------------------------------------------------------- # +def _groundwater_bands(mined: dict, renewable_gw: dict) -> pd.DataFrame: + """Region-indexed band-volume table (the compose groundwater input).""" + regions = sorted(set(mined) | set(renewable_gw)) + return pd.DataFrame( + { + "mined_mm3": [mined.get(r, 0.0) for r in regions], + "renewable_gw_mm3": [renewable_gw.get(r, 0.0) for r in regions], + }, + index=pd.Index(regions, name="region"), + ) + + +def _agri(mm3: dict) -> pd.Series: + return pd.Series(mm3, name="agri_consumption_mm3") + + +def test_annual_bands_are_per_region_gw_only(): + """The bands table is annual per-region groundwater only (surface is separate).""" + tiers = _renewable_tiers() # r1 surface = 60, r2 surface = 20 + bands_in = _groundwater_bands({"r1": 25.0, "r2": 100.0}, {"r1": 10.0, "r2": 50.0}) + agri = _agri({"r1": 40.0, "r2": 12.0}) + out = build_groundwater_bands(tiers, bands_in, agri, ceiling_factor=3.0) + + # GW-only, annual (no surface, no period/tier columns): one row per source. + assert set(out.columns) == {"region", "source", "capacity_mm3", "marginal_cf"} + assert set(out["source"].unique()) == { + "groundwater_renewable", + "groundwater_nonrenewable", + } + + # Renewable groundwater at the full WaterGAP volume (annual, no /T, no clip). + renew_gw = out[out.source == "groundwater_renewable"].set_index("region")[ + "capacity_mm3" + ] + assert renew_gw["r1"] == pytest.approx(10.0) + assert renew_gw["r2"] == pytest.approx(50.0) + + # Non-renewable ceiling = ceiling_factor * annual consumption (annual, not *T). + nonrenew = out[out.source == "groundwater_nonrenewable"].set_index("region")[ + "capacity_mm3" + ] + assert nonrenew["r1"] == pytest.approx(3.0 * 40.0) + assert nonrenew["r2"] == pytest.approx(3.0 * 12.0) + + +def test_annual_bands_merit_cf_and_fallback(): + """Renewable GW at the scarcest surface CF; non-renewable cf 0; the C=0 + fallback sizes the ceiling from the region's surface scale.""" + tiers = _renewable_tiers() + bands_in = _groundwater_bands({"r1": 25.0, "r2": 0.0}, {"r1": 10.0, "r2": 0.0}) + # r1 has a consumption anchor; r2 has none (C absent) -> surface fallback. + agri = _agri({"r1": 40.0}) + out = build_groundwater_bands(tiers, bands_in, agri, ceiling_factor=3.0) + + r1 = out[out.region == "r1"].set_index("source") + # Renewable GW sits at the region's scarcest surface CF (drawn after surface). + assert r1.loc["groundwater_renewable", "marginal_cf"] == pytest.approx(5.0) + assert r1.loc["groundwater_nonrenewable", "marginal_cf"] == pytest.approx(0.0) + + # r2 has no consumption anchor: ceiling falls back to surface scale (20 * 3). + r2_nonrenew = out[(out.region == "r2") & (out.source == "groundwater_nonrenewable")] + assert r2_nonrenew["capacity_mm3"].iloc[0] == pytest.approx(3.0 * 20.0) + + +def test_collapse_single_flat_cap(): + tiers = _renewable_tiers() + out = collapse_single(tiers) + assert (out.groupby("region").size() == 1).all() + assert (out["marginal_cf"] == 0.0).all() + assert (out["source"] == "renewable").all() + assert out.loc[out.region == "r1", "capacity_mm3"].iloc[0] == pytest.approx(60.0) + + +# --------------------------------------------------------------------------- # +# Source-split metrics + solve-time levers (tiny hand-built network) +# --------------------------------------------------------------------------- # +def _network_with_water_tiers() -> pypsa.Network: + n = pypsa.Network() + n.set_snapshots(["now"]) + n.buses.add( + [ + "water:source", + "water:r1", + "impact:water_scarcity", + "impact:groundwater_depletion", + "impact:groundwater_renewable", + ] + ) + # One surface tier (CF=2), one renewable-groundwater tier (CF=3, tallied on + # bus3), and one non-renewable tier (eff2=1) in region r1. + n.links.add( + ["supply:water:r1:t0", "supply:water:r1:t1", "supply:water:r1:t2"], + bus0="water:source", + bus1="water:r1", + bus2=[ + "impact:water_scarcity", + "impact:water_scarcity", + "impact:groundwater_depletion", + ], + bus3=["", "impact:groundwater_renewable", ""], + carrier="water_supply", + efficiency=1.0, + efficiency2=[2.0, 3.0, 1.0], + efficiency3=[0.0, 1.0, 0.0], + region="r1", + source=["renewable", "groundwater_renewable", "groundwater_nonrenewable"], + ) + n.stores.add( + [ + "store:impact:water_scarcity", + "store:impact:groundwater_depletion", + "store:impact:groundwater_renewable", + ], + bus=[ + "impact:water_scarcity", + "impact:groundwater_depletion", + "impact:groundwater_renewable", + ], + e_nom_extendable=True, + ) + # Hand-set dispatch: surface 100 Mm3, renewable GW 20 Mm3, mined 40 Mm3. + n.links.dynamic.p0 = pd.DataFrame( + { + "supply:water:r1:t0": [100.0], + "supply:water:r1:t1": [20.0], + "supply:water:r1:t2": [40.0], + }, + index=n.snapshots, + ) + n.meta["water_consumed_fraction"] = 0.5 + return n + + +def test_metrics_split_three_sources(): + n = _network_with_water_tiers() + totals = extract_water_totals(n) + assert totals["withdrawn_mm3"] == pytest.approx(160.0) # all sources + # Reported withdrawal = consumption / consumed fraction. + assert totals["withdrawal_reported_mm3"] == pytest.approx(320.0) + # Scarcity accrues on CF-carrying tiers: surface (2*100) + renewable GW (3*20). + assert totals["scarcity_mm3_eq"] == pytest.approx(260.0) + assert totals["groundwater_renewable_mm3"] == pytest.approx(20.0) + assert totals["groundwater_depletion_mm3"] == pytest.approx(40.0) + assert totals["mean_cf"] == pytest.approx(260.0 / 120.0) # CF-carrying draw + + +# --------------------------------------------------------------------------- # +# Consumptive-efficiency calibration +# --------------------------------------------------------------------------- # +def test_calibrate_eta_c_anchor_clip_floor(): + """eta_c = E/C, clipped to [eta_min, eta_max] and floored at E/pool.""" + regions = pd.Index( + ["anchor", "low", "deficit", "noisy", "floored", "dry"], name="region" + ) + e = pd.Series([50.0, 10.0, 300.0, 800.0, 50.0, 0.0], index=regions) + c = pd.Series([100.0, 100.0, 100.0, 100.0, 100.0, 0.0], index=regions) + # Pools: generous except 'floored' (pool clipped below observed C) and + # 'dry' (no water at all). + pool = pd.Series([200.0, 200.0, 1000.0, 1000.0, 80.0, 0.0], index=regions) + + eta = calibrate_eta_c(e, c, pool, eta_min=0.2, eta_max=5.0) + assert eta["anchor"] == pytest.approx(0.5) # plain E/C + assert eta["low"] == pytest.approx(0.2) # 0.1 clipped up to eta_min + # Deficit irrigation: E > C is trusted up to eta_max, so the baseline + # draws the observed consumption C = E / eta_c = 100, not the full E. + assert eta["deficit"] == pytest.approx(3.0) + # An unreliably small anchor (E/C = 8) is capped at eta_max. + assert eta["noisy"] == pytest.approx(5.0) + # Floor: E/C = 0.5 would give baseline C = 100 > pool = 80; floored to + # E/pool = 0.625 so the baseline draw exactly fits the pool. + assert eta["floored"] == pytest.approx(50.0 / 80.0) + assert eta["dry"] == pytest.approx(1.0) # no irrigation -> pass-through + + +def test_calibrate_eta_c_infeasible_region_capped(): + """E above eta_max * pool cannot be fixed by efficiency; eta_c caps there.""" + regions = pd.Index(["overshoot"], name="region") + eta = calibrate_eta_c( + pd.Series([600.0], index=regions), + pd.Series([1200.0], index=regions), + pd.Series([100.0], index=regions), + eta_min=0.2, + eta_max=5.0, + ) + assert eta["overshoot"] == pytest.approx(5.0) + + +def test_groundwater_cap_and_pricing_set_store_attributes(): + n = _network_with_water_tiers() + add_groundwater_depletion_cap(n, 0.0) + assert n.stores.static.at["store:impact:groundwater_depletion", "e_nom_max"] == 0.0 + add_groundwater_depletion_pricing_to_objective(n, 0.5) + # 0.5 USD/m3 -> bnUSD/Mm3 = 0.5 / 1e-6 * 1e-9 = 5e-4. + assert n.stores.static.at[ + "store:impact:groundwater_depletion", "marginal_cost_storage" + ] == pytest.approx(5e-4) diff --git a/tests/test_water_surface.py b/tests/test_water_surface.py new file mode 100644 index 00000000..46396d8b --- /dev/null +++ b/tests/test_water_surface.py @@ -0,0 +1,165 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: GPL-3.0-or-later + +"""Unit tests for the WaterGAP surface-availability rescaling. + +The AWARE scarcity (CF) curve is kept, but each (region, month) pool volume is +rescaled to WaterGAP's monthly irrigation surface consumption. The regional +total is fixed by the WaterGAP anchor, while a direct grid-to-basin overlay +sets the within-region basin split. These tests pin conservation, that the +direct split overrides the AWARE-area split, and the no-signal fallbacks. +""" + +import pandas as pd +import pytest + +from workflow.scripts.build_region_water_aware import scale_pool_to_watergap_surface + + +def _cells() -> pd.DataFrame: + """Two regions; r1 has two basins in month 1, one in month 2.""" + return pd.DataFrame( + { + "region": ["r1", "r1", "r1", "r2", "r2"], + "basin_id": [1, 2, 1, 3, 3], + "month": [1, 1, 2, 1, 2], + "region_pool": [30.0, 10.0, 20.0, 5.0, 5.0], + "amd0": [0.1, 0.2, 0.1, 0.3, 0.4], + } + ) + + +def _surface(values: dict) -> pd.Series: + idx = pd.MultiIndex.from_tuples(values.keys(), names=["region", "month"]) + return pd.Series(list(values.values()), index=idx) + + +def _basin_surface(values: dict) -> pd.Series: + idx = pd.MultiIndex.from_tuples( + values.keys(), names=["region", "basin_id", "month"] + ) + return pd.Series(list(values.values()), index=idx) + + +def test_scaled_monthly_totals_match_watergap(): + cells = _cells() + surface = _surface( + {("r1", 1): 4.0, ("r1", 2): 40.0, ("r2", 1): 5.0, ("r2", 2): 0.0} + ) + basin_surface = _basin_surface( + { + ("r1", 1, 1): 8.0, + ("r1", 2, 1): 2.0, + ("r1", 1, 2): 10.0, + ("r2", 3, 1): 1.0, + } + ) + scaled, factor = scale_pool_to_watergap_surface(cells, surface, basin_surface) + + monthly = scaled.groupby(["region", "month"])["region_pool"].sum() + assert monthly[("r1", 1)] == pytest.approx(4.0) # scaled down 10x + assert monthly[("r1", 2)] == pytest.approx(40.0) # scaled up 2x (trusted) + assert monthly[("r2", 1)] == pytest.approx(5.0) # unchanged + assert monthly[("r2", 2)] == pytest.approx(0.0) # WaterGAP delivers nothing + assert factor[("r1", 1, 1)] == pytest.approx(3.2 / 30.0) + assert factor[("r1", 2, 1)] == pytest.approx(0.8 / 10.0) + + +def test_direct_basin_overlay_replaces_aware_area_split_and_preserves_amd0(): + cells = _cells() + surface = _surface( + {("r1", 1): 4.0, ("r1", 2): 20.0, ("r2", 1): 5.0, ("r2", 2): 5.0} + ) + basin_surface = _basin_surface( + { + ("r1", 1, 1): 1.0, + ("r1", 2, 1): 9.0, + ("r1", 1, 2): 1.0, + ("r2", 3, 1): 1.0, + ("r2", 3, 2): 1.0, + } + ) + scaled, _ = scale_pool_to_watergap_surface(cells, surface, basin_surface) + + # amd0 (the CF driver) is untouched. + assert list(scaled["amd0"]) == list(cells["amd0"]) + # The WaterGAP basin 1 : basin 2 split within (r1, month 1) is 1:9, + # rather than the AWARE pool's 30:10 area-weighted split. + r1m1 = scaled[(scaled["region"] == "r1") & (scaled["month"] == 1)] + split = r1m1.set_index("basin_id")["region_pool"] + assert split[1] / split[2] == pytest.approx(1.0 / 9.0) + + +def test_unmapped_surface_delivery_gets_an_explicit_ceiling_cf_tier(): + cells = _cells() + surface = _surface( + { + ("r1", 1): 40.0, + ("r1", 2): 20.0, + ("r2", 1): 5.0, + ("r2", 2): 5.0, + } + ) + basin_surface = _basin_surface({}) + scaled, factor = scale_pool_to_watergap_surface(cells, surface, basin_surface) + + assert factor[("r1", 1, 1)] == pytest.approx(0.0) + assert factor[("r1", 2, 1)] == pytest.approx(0.0) + m3 = scaled[(scaled["region"] == "r1") & (scaled["month"] == 1)] + by_basin = m3.groupby("basin_id")["region_pool"].sum() + assert by_basin.sum() == pytest.approx(40.0) + assert by_basin[-1] == pytest.approx(40.0) + assert m3.set_index("basin_id").at[-1, "amd0"] == pytest.approx(0.0) + + +def test_delivery_to_zero_aware_pool_gets_the_aware_ceiling_cf(): + cells = pd.concat( + [ + _cells(), + pd.DataFrame( + { + "region": ["r1"], + "basin_id": [1], + "month": [3], + "region_pool": [0.0], + "amd0": [0.1], + } + ), + ], + ignore_index=True, + ) + surface = _surface( + { + ("r1", 1): 40.0, + ("r1", 2): 20.0, + ("r1", 3): 8.0, + ("r2", 1): 5.0, + ("r2", 2): 5.0, + } + ) + basin_surface = _basin_surface({("r1", 1, 3): 1.0}) + + scaled, _ = scale_pool_to_watergap_surface(cells, surface, basin_surface) + + m3 = scaled[(scaled["region"] == "r1") & (scaled["month"] == 3)] + assert m3["region_pool"].sum() == pytest.approx(8.0) + assert m3["amd0"].iloc[0] == pytest.approx(0.0) + + +def test_region_without_any_aware_pool_keeps_direct_delivery_at_the_ceiling(): + cells = pd.DataFrame( + { + "region": ["r1", "r1"], + "basin_id": [1, 1], + "month": [1, 2], + "region_pool": [0.0, 0.0], + "amd0": [0.1, 0.2], + } + ) + surface = _surface({("r1", 1): 5.0, ("r1", 2): 3.0}) + basin_surface = _basin_surface({("r1", 1, 1): 5.0, ("r1", 1, 2): 3.0}) + scaled, factor = scale_pool_to_watergap_surface(cells, surface, basin_surface) + assert (factor == 0.0).all() + assert scaled["region_pool"].sum() == pytest.approx(8.0) + assert (scaled["amd0"] == 0.0).all() diff --git a/tests/test_water_temporal.py b/tests/test_water_temporal.py new file mode 100644 index 00000000..35c341e6 --- /dev/null +++ b/tests/test_water_temporal.py @@ -0,0 +1,172 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: GPL-3.0-or-later + +"""Unit tests for the intra-year water temporal resolution. + +Covers the month -> period mapping and per-period convex-curve aggregation in +``compose_water_supply`` and the crop-demand split in ``build_model.crops``. +Both use ``month * T // 12`` equal blocks and must agree; volume/mass must be +conserved regardless of the number of periods. +""" + +import numpy as np +import pandas as pd +import pytest + +from workflow.scripts.build_model.crops import period_demand_shares +from workflow.scripts.compose_water_supply import ( + aggregate_months_to_periods, + build_groundwater_bands, + month_to_period, +) +from workflow.scripts.water_periods import calendar_period_shares, crop_monthly_shares + + +def _shares(start_day: float, length_days: float, temporal_resolution: int): + """Single-season convenience wrapper over the vectorised (N, T) function.""" + return period_demand_shares( + np.array([start_day]), np.array([length_days]), temporal_resolution + )[0] + + +@pytest.mark.parametrize("temporal_resolution", [1, 2, 3, 4, 6, 12]) +def test_month_to_period_equal_blocks(temporal_resolution): + periods = month_to_period(np.arange(1, 13), temporal_resolution) + assert periods.min() == 0 + assert periods.max() == temporal_resolution - 1 + # Every period spans the same number of whole months. + counts = np.bincount(periods) + assert (counts == 12 // temporal_resolution).all() + + +def _monthly_tiers() -> pd.DataFrame: + """One region, two ascending-CF tiers per month.""" + months = list(range(1, 13)) + return pd.DataFrame( + { + "region": ["r"] * 24, + "month": months * 2, + "tier": [0] * 12 + [1] * 12, + "capacity_mm3": [10.0] * 12 + [5.0] * 12, + "marginal_cf": [1.0] * 12 + [4.0] * 12, + } + ) + + +@pytest.mark.parametrize("temporal_resolution", [1, 2, 3, 4, 6, 12]) +def test_aggregate_conserves_volume_and_period_count(temporal_resolution): + monthly = _monthly_tiers() + agg = aggregate_months_to_periods(monthly, temporal_resolution) + assert set(agg["period"].unique()) == set(range(temporal_resolution)) + assert agg["capacity_mm3"].sum() == pytest.approx(monthly["capacity_mm3"].sum()) + # Tiers stay in ascending-CF (merit) order within each period. + for _, group in agg.groupby("period"): + assert group["marginal_cf"].is_monotonic_increasing + + +def test_groundwater_bands_are_annual_per_region(): + # Groundwater is an annual per-region resource (one renewable + one mined band + # per region), independent of the number of periods -- not split /T. + surface = aggregate_months_to_periods(_monthly_tiers(), 4) + groundwater = pd.DataFrame( + {"mined_mm3": [0.0], "renewable_gw_mm3": [40.0]}, + index=pd.Index(["r"], name="region"), + ) + agri = pd.Series({"r": 100.0}) + bands = build_groundwater_bands(surface, groundwater, agri, ceiling_factor=3.0) + renewable = bands[bands["source"] == "groundwater_renewable"] + nonrenewable = bands[bands["source"] == "groundwater_nonrenewable"] + # One annual band each: renewable = full WaterGAP volume; ceiling = 3 * C. + assert set(bands.columns) == {"region", "source", "capacity_mm3", "marginal_cf"} + assert len(renewable) == 1 + assert renewable["capacity_mm3"].iloc[0] == pytest.approx(40.0) + assert len(nonrenewable) == 1 + assert nonrenewable["capacity_mm3"].iloc[0] == pytest.approx(300.0) + # Renewable GW borrows the region's scarcest surface CF; mining is cf 0. + assert renewable["marginal_cf"].iloc[0] == pytest.approx( + surface["marginal_cf"].max() + ) + assert nonrenewable["marginal_cf"].iloc[0] == 0.0 + + +def test_demand_shares_sum_to_one_and_match_period_mapping(): + # A season wholly inside the first quarter lands entirely in period 0. + shares = _shares(1.0, 59.0, 4) + assert shares.sum() == pytest.approx(1.0) + assert shares[0] == pytest.approx(1.0) + + # A season spanning two quarters splits between them, nothing elsewhere. + shares = _shares(91.0, 183.0, 4) + assert shares.sum() == pytest.approx(1.0) + assert shares[1] > 0.4 and shares[2] > 0.4 + assert shares[0] < 0.02 and shares[3] < 0.02 + + +def test_demand_shares_wraps_year_boundary(): + # Nov -> Feb season spends time in period 3 (Oct-Dec) and period 0 (Jan-Mar). + shares = _shares(305.0, 120.0, 4) + assert shares.sum() == pytest.approx(1.0) + assert shares[3] > 0.3 and shares[0] > 0.2 + assert shares[1] == pytest.approx(0.0) and shares[2] == pytest.approx(0.0) + + +def test_demand_shares_missing_season_even_split(): + shares = _shares(np.nan, np.nan, 4) + assert np.allclose(shares, 0.25) + + +def test_demand_share_periods_agree_with_compose_mapping(): + # A short season inside a single calendar month must land in the same period + # compose assigns that month to (both use ``month * T // 12``). + month_lengths = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + month_start = np.concatenate([[1], 1 + np.cumsum(month_lengths)[:-1]]) + for temporal_resolution in [2, 3, 4, 6, 12]: + for month in range(1, 13): + start_day = float(month_start[month - 1] + 3) # safely inside the month + shares = _shares(start_day, 10.0, temporal_resolution) + expected = month_to_period(np.array([month]), temporal_resolution)[0] + assert np.argmax(shares) == expected, (temporal_resolution, month) + + +def test_calendar_period_shares_bins_observed_months(): + # A crop grown entirely in Jan-Mar (rabi) must land in period 0 at T=4, + # overriding a GAEZ summer season; magnitude preserved (shares sum to 1). + monthly = np.zeros((1, 12)) + monthly[0, 0] = monthly[0, 1] = monthly[0, 2] = 1 / 3 # Jan, Feb, Mar + shares, observed = calendar_period_shares( + monthly, np.array([180.0]), np.array([120.0]), 4 + ) + assert observed[0] + assert shares[0, 0] == pytest.approx(1.0) + assert shares[0].sum() == pytest.approx(1.0) + + +def test_calendar_period_shares_falls_back_to_gaez(): + # An all-zero monthly profile (no MIRCA observation) falls back to the GAEZ + # growing-season split. + monthly = np.zeros((1, 12)) + shares, observed = calendar_period_shares( + monthly, np.array([1.0]), np.array([59.0]), 4 + ) + assert not observed[0] + gaez = period_demand_shares(np.array([1.0]), np.array([59.0]), 4) + assert np.allclose(shares, gaez) + + +def test_crop_monthly_shares_pivots_and_fills_missing_regions(): + calendar = pd.DataFrame( + { + "region": ["r1", "r1", "r2"], + "crop": ["wheat", "wheat", "wheat"], + "month": [1, 2, 7], + "share": [0.6, 0.4, 1.0], + } + ) + out = crop_monthly_shares(calendar, "wheat", np.array(["r1", "r2", "r3"])) + assert out.shape == (3, 12) + assert out[0, 0] == pytest.approx(0.6) and out[0, 1] == pytest.approx(0.4) + assert out[1, 6] == pytest.approx(1.0) # r2, July + assert out[2].sum() == pytest.approx(0.0) # r3 absent -> all zero (fallback) + # A crop with no rows returns an all-zero matrix. + assert crop_monthly_shares(calendar, "maize", np.array(["r1"])).sum() == 0.0 diff --git a/workflow/rules/crops.smk b/workflow/rules/crops.smk index b3109ecc..bc28a366 100644 --- a/workflow/rules/crops.smk +++ b/workflow/rules/crops.smk @@ -674,12 +674,19 @@ def multi_cropping_inputs(_wildcards): "cell_mapping": "/{name}/region_class_cell_mapping.npz", "yield_unit_conversions": "data/curated/yield_unit_conversions.csv", "combinations": multicropping_combinations_yaml(), + "crop_calendar": "/{name}/water/mirca_crop_calendar.csv", } for ws in ("r", "i"): for crop in sorted(crops_by_supply[ws]): prefix = f"{crop}_{ws}" inputs[f"{prefix}_yield_raster"] = gaez_path(yield_kind, ws, crop) inputs[f"{prefix}_suitability_raster"] = gaez_path("suitability", ws, crop) + inputs[f"{prefix}_growing_season_start_raster"] = gaez_path( + "growing_season_start", ws, crop + ) + inputs[f"{prefix}_growing_season_length_raster"] = gaez_path( + "growing_season_length", ws, crop + ) if ws == "i": inputs[f"{prefix}_water_requirement_raster"] = gaez_path( "water_requirement", ws, crop @@ -697,6 +704,7 @@ rule build_multi_cropping: moisture_content="data/curated/crop_moisture_content.csv", params: use_actual_yields=config["validation"]["use_actual_yields"], + water_periods=config["water"]["temporal_resolution"], output: eligible="/{name}/multi_cropping/eligible_area.csv", yields="/{name}/multi_cropping/cycle_yields.csv", diff --git a/workflow/rules/model.smk b/workflow/rules/model.smk index 146939f0..43854553 100644 --- a/workflow/rules/model.smk +++ b/workflow/rules/model.smk @@ -174,9 +174,10 @@ rule build_model: costs="/{name}/faostat_crop_costs.csv", animal_costs="/{name}/animal_costs.csv", grassland_yields="/{name}/grassland_yields.csv", - monthly_region_water="/{name}/water/monthly_region_water.csv", - growing_season_water="/{name}/water/region_growing_season_water.csv", - blue_water_availability="/{name}/water/blue_water_availability.csv", + water_tiers="/{name}/water/region_water_tiers.csv", + groundwater_bands="/{name}/water/region_groundwater_bands.csv", + region_agri_consumption="/{name}/water/watergap/region_agri_consumption.csv", + mirca_crop_calendar="/{name}/water/mirca_crop_calendar.csv", luc_carbon_coefficients="/{name}/luc/luc_carbon_coefficients.csv", faostat_pasture_area="/{name}/faostat_pasture_area.csv", current_grassland_area="/{name}/luc/current_grassland_area_by_class.csv", @@ -450,6 +451,28 @@ rule solve_model: ghg_pricing_enabled=lambda w: get_effective_config(w.scenario)["emissions"][ "ghg_pricing_enabled" ], + water_scarcity_tiers=config["water"]["supply"]["scarcity_tiers"], + water_scarcity_pricing_enabled=lambda w: get_effective_config(w.scenario)[ + "water_scarcity" + ]["pricing_enabled"], + water_scarcity_price=lambda w: get_effective_config(w.scenario)[ + "water_scarcity" + ]["price"], + water_scarcity_cap=lambda w: get_effective_config(w.scenario)[ + "water_scarcity" + ]["cap_mm3_world_eq"], + water_scarcity_nonrenewable_cf=lambda w: get_effective_config(w.scenario)[ + "water_scarcity" + ]["nonrenewable_cf"], + groundwater_pricing_enabled=lambda w: get_effective_config(w.scenario)[ + "groundwater_depletion" + ]["pricing_enabled"], + groundwater_price=lambda w: get_effective_config(w.scenario)[ + "groundwater_depletion" + ]["price"], + groundwater_cap=lambda w: get_effective_config(w.scenario)[ + "groundwater_depletion" + ]["cap_mm3"], food_incentives_enabled=lambda w: get_effective_config(w.scenario)[ "food_incentives" ]["enabled"], diff --git a/workflow/rules/retrieve.smk b/workflow/rules/retrieve.smk index 60226b39..88891911 100644 --- a/workflow/rules/retrieve.smk +++ b/workflow/rules/retrieve.smk @@ -947,18 +947,21 @@ rule extract_mirca_os_subcrop_monthly: "../scripts/extract_mirca_os_archive.py" -rule download_aware2_basins: - """Download the AWARE2.0 native-basin polygons from Zenodo. +rule download_aware2: + """Download the AWARE2.0 water-scarcity dataset from Zenodo. Source: Seitfudem, Berger, Mueller Schmied & Boulay (2025), "The updated and improved method for water scarcity impact assessment in LCA, AWARE2.0", https://doi.org/10.5281/zenodo.15133241 (WaterGAP2.2e). License: CC BY 4.0. - The geopackage carries the native basin geometries and their - characterisation factors; the annual agricultural CF is used as the basin - scarcity signal for basin-aware region clustering. + Three files are used downstream: the intermediate hydrological variables + (naturalised availability, environmental reserve, basin area, AMD), the + native characterisation factors with sectoral water demand, and the + native-basin polygons for the basin-to-region crosswalk. """ output: + intermediate="data/downloads/aware2/AWARE20_Intermediate_Variables.xlsx", + native_cfs="data/downloads/aware2/AWARE20_Native_CFs.xlsx", basins="data/downloads/aware2/AWARE20_Native_CFs_geospatial.gpkg", params: base_url="https://zenodo.org/records/15133241/files", @@ -966,14 +969,86 @@ rule download_aware2_basins: runtime="30m", mem_mb=500, log: - "/shared/download_aware2_basins.log", + "/shared/download_aware2.log", benchmark: - "/shared/download_aware2_basins.tsv" + "/shared/download_aware2.tsv" shell: r""" - mkdir -p "$(dirname {output.basins})" + mkdir -p "$(dirname {output.intermediate})" + curl -L --fail --progress-bar -o "{output.intermediate}" \ + "{params.base_url}/AWARE20_Intermediate_Variables.xlsx/content" > {log} 2>&1 + curl -L --fail --progress-bar -o "{output.native_cfs}" \ + "{params.base_url}/AWARE20_Native_CFs.xlsx/content" >> {log} 2>&1 curl -L --fail --progress-bar -o "{output.basins}" \ - "{params.base_url}/AWARE20_Native_CFs_geospatial.gpkg/content" > {log} 2>&1 + "{params.base_url}/AWARE20_Native_CFs_geospatial.gpkg/content" >> {log} 2>&1 + """ + + +rule download_watergap_isimip: + """Download WaterGAP 2.2e water fields from the ISIMIP3a repository. + + Source: Mueller Schmied et al. (2024), "The global water resources and use + model WaterGAP v2.2e", Geosci. Model Dev. 17, 8817-8852, + https://doi.org/10.5194/gmd-17-8817-2024. Standard ISIMIP3a model output, + obsclim climate / histsoc (with human water use) setup, gswp3-w5e5 forcing, + monthly 1901-2019, 0.5 degree. License: CC BY 4.0. The ISIMIP repository + (https://data.isimip.org) serves the full, documented variable set as stable + public files under files.isimip.org. + + We fetch the groundwater storage compartment (``groundwstor``; its negative + long-term trend is groundwater depletion, Doll et al. 2014) and the potential + irrigation water consumption, total (``pirruse``) and from groundwater + (``pirrusegw``). The surface part (``pirruse - pirrusegw``) is WaterGAP's + irrigation surface availability, used to cap the AWARE scarcity curve; + ``pirrusegw`` gives the renewable groundwater band. + """ + output: + groundwstor="data/downloads/watergap/watergap2-2e_gswp3-w5e5_obsclim_histsoc_default_groundwstor_global_monthly_1901_2019.nc", + pirruse="data/downloads/watergap/watergap2-2e_gswp3-w5e5_obsclim_histsoc_default_pirruse_global_monthly_1901_2019.nc", + pirrusegw="data/downloads/watergap/watergap2-2e_gswp3-w5e5_obsclim_histsoc_default_pirrusegw_global_monthly_1901_2019.nc", + params: + base_url="https://files.isimip.org/ISIMIP3a/OutputData/water_global/WaterGAP2-2e/gswp3-w5e5/historical", + resources: + runtime="30m", + mem_mb=500, + log: + "/shared/download_watergap_isimip.log", + benchmark: + "/shared/download_watergap_isimip.tsv" + shell: + r""" + mkdir -p "$(dirname {output.groundwstor})" + for out in "{output.groundwstor}" "{output.pirruse}" "{output.pirrusegw}"; do + curl -L --fail --progress-bar -o "$out" \ + "{params.base_url}/$(basename "$out")" >> {log} 2>&1 + done + """ + + +rule download_watergap_continental_area: + """Download WaterGAP's static continental-area grid. + + Source: Mueller Schmied et al. (2024), WaterGAP v2.2e standard output, + https://doi.org/10.5194/gmd-17-8817-2024. The field is published with the + gswp3-w5e5 histsoc output under https://doi.org/10.25716/GUDE.0TNY-KJPG. + It converts WaterGAP's per-area fluxes and storage changes to volume. + License: CC BY 4.0. + """ + output: + "data/downloads/watergap/watergap22e_gswp3-w5e5_continentalarea_histsoc_static.nc", + params: + url="https://api.gude.uni-frankfurt.de/api/core/bitstreams/0ba40ee1-4e40-481b-9ffe-811b3e786597/content", + resources: + runtime="5m", + mem_mb=200, + log: + "/shared/download_watergap_continental_area.log", + benchmark: + "/shared/download_watergap_continental_area.tsv" + shell: + r""" + mkdir -p "$(dirname {output})" + curl -L --fail --progress-bar -o "{output}" "{params.url}" > {log} 2>&1 """ diff --git a/workflow/rules/water.smk b/workflow/rules/water.smk index dfe7f143..80f19605 100644 --- a/workflow/rules/water.smk +++ b/workflow/rules/water.smk @@ -9,11 +9,28 @@ Includes fertilizer application rates, blue water availability, and regional water resource calculations. Water supply scenario: -- "sustainable": Uses Water Footprint Network blue water availability (Hoekstra & Mekonnen 2011) +- "aware": Uses AWARE2.0 (Seitfudem et al. 2025, WaterGAP2.2e) naturalised + availability and a convex water-scarcity supply curve. - "current_use": Uses Huang et al. (2018) gridded irrigation water withdrawals + (validation/benchmarking; a single zero-scarcity supply tier). + +Both scenarios emit a ``region_water_tiers.csv`` describing the regional water +supply as one or more tiers (capacity in Mm3, marginal scarcity characterisation +factor); ``aware`` resolves it to a convex merit-order curve while ``current_use`` +emits a single zero-CF tier reproducing a hard availability cap. """ +_WATERGAP_ISIMIP = ( + "data/downloads/watergap/" + "watergap2-2e_gswp3-w5e5_obsclim_histsoc_default_{var}_global_monthly_1901_2019.nc" +) +_WATERGAP_CONTINENTALAREA = ( + "data/downloads/watergap/" + "watergap22e_gswp3-w5e5_continentalarea_histsoc_static.nc" +) + + rule prepare_fertilizer_application_rates: input: fubc_data="data/bundled/doi_10_5061_dryad_2rbnzs7qh__v20250311/FUBC_1_to_9_data.csv", @@ -99,27 +116,38 @@ def crop_yield_file_list(w): return list(yield_inputs(w).values()) -# Rule for sustainable water availability (Water Footprint Network data) -rule build_region_water_sustainable: +# Rule for the convex water-scarcity supply curve. The volumes (per +# region-month) are WaterGAP's irrigation surface consumption; its grid cells +# set the within-region AWARE-basin allocation, while AWARE contributes the CF +# curve. +rule build_region_water_aware: input: - shapefile=rules.extract_waterfootprint_appendix.output.shapefile, + intermediate="data/downloads/aware2/AWARE20_Intermediate_Variables.xlsx", + native_cfs="data/downloads/aware2/AWARE20_Native_CFs.xlsx", + basins="data/downloads/aware2/AWARE20_Native_CFs_geospatial.gpkg", regions="/{name}/regions.geojson", - monthly="/{name}/water/blue_water_availability.csv", - crop_yields=crop_yield_file_list, + watergap_surface="/{name}/water/watergap/region_watergap_surface.csv", + watergap_pirruse=_WATERGAP_ISIMIP.format(var="pirruse"), + watergap_pirrusegw=_WATERGAP_ISIMIP.format(var="pirrusegw"), + watergap_continentalarea=_WATERGAP_CONTINENTALAREA, + params: + surface_start=config["water"]["data"]["surface_reference_start"], + surface_end=config["water"]["data"]["surface_reference_end"], output: - monthly_region="/{name}/water/sustainable/monthly_region_water.csv", - region_growing="/{name}/water/sustainable/region_growing_season_water.csv", + monthly_region="/{name}/water/aware/monthly_region_water.csv", + region_growing="/{name}/water/aware/region_growing_season_water.csv", + tiers="/{name}/water/aware/region_water_tiers.csv", group: "prep" resources: - runtime="1m", - mem_mb=300, + runtime="15m", + mem_mb=2000, log: - "/{name}/build_region_water_sustainable.log", + "/{name}/build_region_water_aware.log", benchmark: - "/{name}/build_region_water_sustainable.tsv" + "/{name}/build_region_water_aware.tsv" script: - "../scripts/build_region_water_availability.py" + "../scripts/build_region_water_aware.py" # Rule for current water use (Huang et al. 2018 gridded irrigation data) @@ -129,10 +157,11 @@ rule build_region_water_current_use: regions="/{name}/regions.geojson", crop_yields=crop_yield_file_list, params: - reference_year=config["water"]["huang_reference_year"], + reference_year=config["water"]["data"]["huang_reference_year"], output: monthly_region="/{name}/water/current_use/monthly_region_water.csv", region_growing="/{name}/water/current_use/region_growing_season_water.csv", + tiers="/{name}/water/current_use/region_water_tiers.csv", group: "prep" resources: @@ -146,47 +175,156 @@ rule build_region_water_current_use: "../scripts/process_huang_irrigation_water.py" -def water_monthly_input(w): - """Select monthly water input based on config.""" - scenario = config["water"]["supply_scenario"] - return f"/{w.name}/water/{scenario}/monthly_region_water.csv" +# Rule for regional WaterGAP 2.2e (ISIMIP3a) water fields: irrigation surface +# availability (pirruse - pirrusegw, the cap for the AWARE curve) and the +# groundwater bands (mined = storage trend; renewable = pirrusegw - mined). +rule build_region_watergap: + input: + groundwstor=_WATERGAP_ISIMIP.format(var="groundwstor"), + pirruse=_WATERGAP_ISIMIP.format(var="pirruse"), + pirrusegw=_WATERGAP_ISIMIP.format(var="pirrusegw"), + continentalarea=_WATERGAP_CONTINENTALAREA, + regions="/{name}/regions.geojson", + params: + trend_start=config["water"]["data"]["groundwater_trend_start"], + trend_end=config["water"]["data"]["groundwater_trend_end"], + surface_start=config["water"]["data"]["surface_reference_start"], + surface_end=config["water"]["data"]["surface_reference_end"], + output: + surface="/{name}/water/watergap/region_watergap_surface.csv", + depletion="/{name}/water/watergap/region_groundwater_depletion.csv", + region_agri="/{name}/water/watergap/region_agri_consumption.csv", + demand="/{name}/water/watergap/region_watergap_demand.csv", + group: + "prep" + resources: + runtime="5m", + mem_mb=2000, + log: + "/{name}/build_region_watergap.log", + benchmark: + "/{name}/build_region_watergap.tsv" + script: + "../scripts/build_region_watergap.py" + + +# MIRCA-OS v2 crops with more than one irrigated sub-crop cycle in the monthly +# grids; every other base crop ships a single grid without a cycle digit. Used +# to expand each mapped base crop into its sub-crop grid filenames. +_MIRCA_SUBCROP_CYCLES = {"Rice": 3, "Wheat": 2} + + +def mirca_calendar_grids(w): + """2015 irrigated monthly growing-area grids for every mapped MIRCA crop. + Reads the crop concordance plus the calendar-only supplement and expands + each non-dropped MIRCA base crop into its sub-crop grid(s) (``Rice1/2/3``, + ``Wheat1/2``, else the base label), keyed ``nc_{subcrop}`` for + ``build_mirca_crop_calendar``. + """ + import pandas as pd -def water_growing_input(w): - """Select growing season water input based on config.""" - scenario = config["water"]["supply_scenario"] - return f"/{w.name}/water/{scenario}/region_growing_season_water.csv" + mapping = pd.concat( + [ + pd.read_csv("data/curated/mirca_os_crop_mapping.csv", comment="#"), + pd.read_csv("data/curated/mirca_os_calendar_supplement.csv", comment="#"), + ], + ignore_index=True, + ) + mapping = mapping[mapping["glade_crop"].notna() & (mapping["glade_crop"] != "")] + grids = {} + for base in mapping["mirca_crop"].unique(): + n = _MIRCA_SUBCROP_CYCLES.get(base, 1) + labels = [f"{base}{i}" for i in range(1, n + 1)] if n > 1 else [base] + for label in labels: + grids[f"nc_{label}"] = ( + f"data/downloads/mirca_os/grids/monthly/MIRCA-OS_{label}_2015_ir.nc" + ) + return grids -# Unified rule that creates symlinks to the selected water data source -# This maintains backward compatibility with existing downstream rules -rule select_water_scenario: +# Observed irrigated crop calendar: per (region, crop) monthly water-demand +# shares from MIRCA-OS 2015 monthly growing-area grids, retimed by iterative +# proportional fitting so that region-month demand totals follow WaterGAP's +# monthly irrigation requirement (pirruse) while each crop's annual total and +# observed season (structural zeros) are preserved. Places irrigation demand +# in the observed months at requirement weighting (single-crop links and +# multi-cropping cycles), consistent with WaterGAP's reservoir-regulated +# monthly surface delivery, instead of GAEZ's yield-maximising potential +# calendar with uniform within-season weighting. +rule build_mirca_crop_calendar: input: - monthly=water_monthly_input, - growing=water_growing_input, + unpack(mirca_calendar_grids), + mapping="data/curated/mirca_os_crop_mapping.csv", + supplement="data/curated/mirca_os_calendar_supplement.csv", + demand="/{name}/water/watergap/region_watergap_demand.csv", + crop_yields=crop_yield_file_list, + regions="/{name}/regions.geojson", + output: + "/{name}/water/mirca_crop_calendar.csv", + group: + "prep" + resources: + runtime="10m", + mem_mb=3000, + log: + "/{name}/build_mirca_crop_calendar.log", + benchmark: + "/{name}/build_mirca_crop_calendar.tsv" + script: + "../scripts/build_mirca_crop_calendar.py" + + +def water_availability_inputs(w): + """The availability source's monthly/growing/tier files (aware or current_use).""" + availability = config["water"]["data"]["availability"] + base = f"/{w.name}/water/{availability}" + return { + "monthly": f"{base}/monthly_region_water.csv", + "growing": f"{base}/region_growing_season_water.csv", + "tiers": f"{base}/region_water_tiers.csv", + } + + +def water_groundwater_input(w): + """Groundwater depletion table and consumption anchor (groundwater only).""" + if config["water"]["supply"]["groundwater"]: + return { + "groundwater": f"/{w.name}/water/watergap/region_groundwater_depletion.csv", + "region_agri": f"/{w.name}/water/watergap/region_agri_consumption.csv", + } + return {} + + +# Compose the scenario-agnostic water-supply tables from the selected +# availability source, tagging each tier with a `source` (renewable | +# groundwater). +rule compose_water_supply: + input: + unpack(water_availability_inputs), + unpack(water_groundwater_input), + params: + scarcity_tiers=config["water"]["supply"]["scarcity_tiers"], + groundwater=config["water"]["supply"]["groundwater"], + availability=config["water"]["data"]["availability"], + temporal_resolution=config["water"]["temporal_resolution"], + consumed_fraction=config["water"]["irrigation"]["consumed_fraction"], + groundwater_ceiling_factor=config["water"]["supply"][ + "groundwater_ceiling_factor" + ], output: monthly_region="/{name}/water/monthly_region_water.csv", region_growing="/{name}/water/region_growing_season_water.csv", + tiers="/{name}/water/region_water_tiers.csv", + groundwater_bands="/{name}/water/region_groundwater_bands.csv", group: "prep" resources: runtime="1m", mem_mb=400, log: - "/{name}/select_water_scenario.log", + "/{name}/compose_water_supply.log", benchmark: - "/{name}/select_water_scenario.tsv" - run: - import shutil - from pathlib import Path - - Path(output.monthly_region).parent.mkdir(parents=True, exist_ok=True) - - # Copy files (Snakemake handles symlinks poorly across rules) - shutil.copy(input.monthly, output.monthly_region) - shutil.copy(input.growing, output.region_growing) - - with open(log[0], "w") as f: - f.write(f"Water supply scenario: {config['water']['supply_scenario']}\n") - f.write(f"Copied {input.monthly} -> {output.monthly_region}\n") - f.write(f"Copied {input.growing} -> {output.region_growing}\n") + "/{name}/compose_water_supply.tsv" + script: + "../scripts/compose_water_supply.py" diff --git a/workflow/scripts/analysis/analyze_model.py b/workflow/scripts/analysis/analyze_model.py index ec6fca3c..000f6f54 100644 --- a/workflow/scripts/analysis/analyze_model.py +++ b/workflow/scripts/analysis/analyze_model.py @@ -51,6 +51,7 @@ extract_land_use, extract_luc_breakdown, ) +from workflow.scripts.analysis.extract_water_metrics import extract_water_by_region from workflow.scripts.logging_config import setup_script_logging from workflow.scripts.snakemake_utils import load_solved_network from workflow.scripts.solve_namespace import ANALYSIS_OUTPUT_NAMES @@ -149,6 +150,10 @@ def run_analysis( logger.info("Extracting objective breakdown...") objective_breakdown = extract_objective_breakdown(n) + # --- Water metrics (scarcity / withdrawal / groundwater per region) --- + logger.info("Extracting water metrics...") + water_metrics = extract_water_by_region(n) + # --- GHG attribution --- logger.info("Computing GHG attribution...") food_groups = pd.read_csv(food_groups_path) @@ -215,6 +220,7 @@ def run_analysis( "luc_breakdown": luc_breakdown, "baseline_deviation": baseline_deviation, "food_prices": food_prices, + "water_metrics": water_metrics, } # Producer-side drift guard: every output declared in the # canonical list must be produced here, and we must not produce diff --git a/workflow/scripts/analysis/extract_objective_breakdown.py b/workflow/scripts/analysis/extract_objective_breakdown.py index 0a3e7e00..72cee46a 100644 --- a/workflow/scripts/analysis/extract_objective_breakdown.py +++ b/workflow/scripts/analysis/extract_objective_breakdown.py @@ -89,6 +89,7 @@ def _objective_category(n: pypsa.Network, component: str, **_) -> pd.Series: categories[names.str.startswith("sink:")] = "Biomass exports" categories[names.str.startswith("slack:")] = "Slack penalties" categories[carriers == "fertilizer"] = "Fertilizer" + categories[carriers == "water_source"] = "Water" categories[names.str.startswith("supply:") & categories.isna()] = ( "Resource supply" ) @@ -128,6 +129,9 @@ def _objective_category(n: pypsa.Network, component: str, **_) -> pd.Series: "fiber_demand": "Biomass routing", "emission_aggregation": "Emissions aggregation", "fertilizer_distribution": "Fertilizer", + "water_supply": "Water", + "irrigation_delivery": "Water", + "groundwater_delivery": "Water", } carriers = static["carrier"].astype(str) categories = carriers.map(carrier_map) @@ -147,6 +151,12 @@ def _objective_category(n: pypsa.Network, component: str, **_) -> pd.Series: exact_map = { "ghg": "GHG cost", "water": "Water", + "water_supply": "Water", + "irrigation_delivery": "Water", + "groundwater_delivery": "Water", + "groundwater_renewable": "Water", + "water_scarcity": "Water scarcity cost", + "groundwater_depletion": "Groundwater depletion cost", "fertilizer": "Fertilizer", "spared_land": "Crop production", "spared_grassland": "Crop production", @@ -386,6 +396,8 @@ def _to_series(df_or_series: pd.DataFrame | pd.Series) -> pd.Series: "Emissions aggregation": "emissions_aggregation", "Land use": "land_use", "Water": "water", + "Water scarcity cost": "water_scarcity_cost", + "Groundwater depletion cost": "groundwater_depletion_cost", "Production stability": "production_stability", "Diet stability": "diet_stability", } diff --git a/workflow/scripts/analysis/extract_water_metrics.py b/workflow/scripts/analysis/extract_water_metrics.py new file mode 100644 index 00000000..c91130df --- /dev/null +++ b/workflow/scripts/analysis/extract_water_metrics.py @@ -0,0 +1,150 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: GPL-3.0-or-later + +"""Extract AWARE water-scarcity metrics from solved networks. + +The tiered water supply (carrier ``water_supply``) converts a free source bus +into regional water (bus1 = ``water:{region}``) and tallies scarcity onto +``impact:water_scarcity`` via ``efficiency2`` = the tier's marginal +characterisation factor (CF, m3 world-equivalent per m3). Drawing the link +(``p0`` > 0) therefore both supplies irrigation water and accumulates scarcity. + +Tiers carry a ``source``: ``renewable`` (surface) and ``groundwater_renewable`` +tiers accumulate AWARE scarcity via ``efficiency2`` = the marginal CF (the +latter additionally tally their drawn volume on +``impact:groundwater_renewable``), while ``groundwater_nonrenewable`` tiers +accumulate mined volume 1:1 on ``impact:groundwater_depletion`` (their +``efficiency2`` is 1, not a CF). This module reads those links to report, per +region and in total: +- withdrawn irrigation volume (Mm3), all sources, +- accumulated water scarcity (Mm3 world-equivalent) = sum(CF * draw) over + CF-carrying (surface + renewable groundwater) tiers, +- the volume-weighted mean CF of the CF-carrying water drawn, +- renewable groundwater volume (Mm3) from ``groundwater_renewable`` tiers, +- non-renewable groundwater depletion (Mm3 mined) from + ``groundwater_nonrenewable`` tiers. + +It also exposes the per-tier (CF, draw, source) table used to bin withdrawn +water by the scarcity of its source (the merit-order / withdrawal-by-CF figure). + +Basis: the tier draw is *consumption* C -- crops need their net requirement E +and the ``irrigation_delivery`` link draws ``C = E / eta_c`` from the pool. +``withdrawn_mm3`` therefore reports consumption; the estimated physical +withdrawal is ``withdrawal_reported_mm3 = withdrawn_mm3 / consumed_fraction`` +with the consumed fraction (C/W) stashed in ``n.meta`` at build time. + +Units: the produce-link identity ``m3/ha * Mha = Mm3`` means tier draws and +the scarcity tally are already in Mm3 and Mm3-world-eq (see the AWARE design +note); no scale factor is applied here. The global scarcity and depletion +stores (``store:impact:water_scarcity`` / ``store:impact:groundwater_depletion``) +equal ``scarcity_mm3_eq`` / ``groundwater_depletion_mm3`` summed over regions, +modulo solver tolerance. +""" + +import pandas as pd +import pypsa + +NONRENEWABLE = "groundwater_nonrenewable" +RENEWABLE_GW = "groundwater_renewable" + + +def _water_supply_draw(n: pypsa.Network) -> pd.DataFrame: + """Return a per-tier-link frame with region, CF, drawn volume (Mm3), source.""" + links = n.links.static + tiers = links[links["carrier"] == "water_supply"] + if tiers.empty: + # Configs with no tiered supply: return an empty frame so the analysis + # pipeline stays config-agnostic; callers that require it check emptiness. + return pd.DataFrame(columns=["region", "cf", "draw_mm3", "source"]) + draw = n.links.dynamic.p0.iloc[0].reindex(tiers.index).clip(lower=0.0) + # Pre-source solved networks had only renewable tiers. + source = tiers["source"].to_numpy() if "source" in tiers.columns else "renewable" + return pd.DataFrame( + { + "region": tiers["region"].to_numpy(), + "cf": tiers["efficiency2"].to_numpy(), + "draw_mm3": draw.to_numpy(), + "source": source, + }, + index=tiers.index, + ) + + +def extract_water_tiers(n: pypsa.Network) -> pd.DataFrame: + """Per-tier (region, cf, draw_mm3) table; the input to CF-binned views. + + One row per ``water_supply`` link. ``cf`` is the tier's marginal + characterisation factor; ``draw_mm3`` is the volume drawn from it. + """ + return _water_supply_draw(n).reset_index(names="link") + + +def extract_water_by_region(n: pypsa.Network) -> pd.DataFrame: + """Per-region water metrics. + + Columns: region, withdrawn_mm3 (all sources), scarcity_mm3_eq (CF-carrying + tiers: surface + renewable groundwater), groundwater_renewable_mm3, + groundwater_depletion_mm3 (non-renewable tiers), mean_cf (the draw-weighted + mean CF of the CF-carrying water; NaN where none is drawn). + """ + tiers = _water_supply_draw(n) + is_nonrenew = tiers["source"] == NONRENEWABLE + cf_carrying = tiers[~is_nonrenew].copy() + cf_carrying["scarcity"] = cf_carrying["cf"] * cf_carrying["draw_mm3"] + + regions = pd.Index(sorted(tiers["region"].unique()), name="region") + withdrawn = tiers.groupby("region")["draw_mm3"].sum() + cf_withdrawn = cf_carrying.groupby("region")["draw_mm3"].sum() + scarcity = cf_carrying.groupby("region")["scarcity"].sum() + renewable_gw = ( + tiers[tiers["source"] == RENEWABLE_GW].groupby("region")["draw_mm3"].sum() + ) + depletion = tiers[is_nonrenew].groupby("region")["draw_mm3"].sum() + + grouped = pd.DataFrame(index=regions) + grouped["withdrawn_mm3"] = withdrawn.reindex(regions).fillna(0.0) + grouped["withdrawal_reported_mm3"] = grouped["withdrawn_mm3"] / _consumed_fraction( + n + ) + grouped["scarcity_mm3_eq"] = scarcity.reindex(regions).fillna(0.0) + grouped["groundwater_renewable_mm3"] = renewable_gw.reindex(regions).fillna(0.0) + grouped["groundwater_depletion_mm3"] = depletion.reindex(regions).fillna(0.0) + cfw = cf_withdrawn.reindex(regions).fillna(0.0) + grouped["mean_cf"] = (grouped["scarcity_mm3_eq"] / cfw).where(cfw > 0) + return grouped.reset_index() + + +def _consumed_fraction(n: pypsa.Network) -> float: + """The build-time consumed fraction C/W used for withdrawal reporting.""" + return float(n.meta["water_consumed_fraction"]) + + +def extract_water_totals(n: pypsa.Network) -> pd.Series: + """Global totals: withdrawn_mm3, scarcity_mm3_eq, groundwater_renewable_mm3, + groundwater_depletion_mm3, mean_cf. + + ``scarcity_mm3_eq`` / ``groundwater_depletion_mm3`` match the + ``store:impact:water_scarcity`` / ``store:impact:groundwater_depletion`` + final values (modulo solver tolerance); we recompute them from the tier + draws so the same code path also yields the regional and per-tier breakdowns. + """ + tiers = _water_supply_draw(n) + is_nonrenew = tiers["source"] == NONRENEWABLE + cf_carrying = tiers[~is_nonrenew] + withdrawn = float(tiers["draw_mm3"].sum()) + cf_withdrawn = float(cf_carrying["draw_mm3"].sum()) + scarcity = float((cf_carrying["cf"] * cf_carrying["draw_mm3"]).sum()) + renewable_gw = float(tiers.loc[tiers["source"] == RENEWABLE_GW, "draw_mm3"].sum()) + depletion = float(tiers.loc[is_nonrenew, "draw_mm3"].sum()) + mean_cf = scarcity / cf_withdrawn if cf_withdrawn > 0 else float("nan") + return pd.Series( + { + "withdrawn_mm3": withdrawn, + "withdrawal_reported_mm3": withdrawn / _consumed_fraction(n), + "scarcity_mm3_eq": scarcity, + "groundwater_renewable_mm3": renewable_gw, + "groundwater_depletion_mm3": depletion, + "mean_cf": mean_cf, + } + ) diff --git a/workflow/scripts/build_crop_yields.py b/workflow/scripts/build_crop_yields.py index 4319492f..563e9b4a 100644 --- a/workflow/scripts/build_crop_yields.py +++ b/workflow/scripts/build_crop_yields.py @@ -92,7 +92,11 @@ def _yield_multiplier(crop: str) -> float: water_raw_mm, water_src = read_raster_float(water_path) validate_raster_grid(water_raw_mm, water_src, mapping) water_src.close() - water_m3_per_ha = water_raw_mm * 10.0 # 1 mm over 1 ha equals 10 m3 + # GAEZ RES05-WDC net irrigation requirement carries a handful of small + # negative cells (interpolation artifacts); a negative net requirement + # is physically meaningless (rainfall covers crop ET), and downstream + # it flips the sign of crop water ports into spurious water producers. + water_m3_per_ha = np.clip(water_raw_mm, 0.0, None) * 10.0 # mm -> m3/ha water_by_group = weighted_mean_by_group(water_m3_per_ha, mapping) del water_raw_mm, water_m3_per_ha else: diff --git a/workflow/scripts/build_mirca_crop_calendar.py b/workflow/scripts/build_mirca_crop_calendar.py new file mode 100644 index 00000000..719fad7d --- /dev/null +++ b/workflow/scripts/build_mirca_crop_calendar.py @@ -0,0 +1,431 @@ +""" +SPDX-FileCopyrightText: 2026 Koen van Greevenbroek + +SPDX-License-Identifier: GPL-3.0-or-later + +Build per (region, crop) monthly irrigated water-demand shares from the +MIRCA-OS 2015 monthly growing-area grids, retimed to WaterGAP's monthly +irrigation requirement. + +The water supply envelope is WaterGAP's reservoir-regulated monthly delivery, +but crop water demand was placed in the year by GAEZ growing seasons -- the +potential, yield-maximising calendar, which systematically disagrees with the +observed cropping calendar in the major irrigated systems (Indus, Nile, +Gangetic plain). That demand-calendar mismatch strands deliverable surface +water in months the model does not demand it and mines groundwater in the +months it does. + +MIRCA-OS publishes the observed calendar spatialised: monthly growing-area +grids per crop and sub-crop (5 arcmin, ha per cell-month, planting to +maturity). The **2015** vintage is used deliberately: the 2020 crop calendar +misplaces the entire northwest-India wheat belt (~16.5 Mha) into the monsoon +window, and MIRCA2000 v1.1 splits the same belt 50/50 between monsoon and +rabi windows; the 2015 calendar carries the correct rabi timing (verified +against Sacks et al. 2010 and USDA FAS calendars). Calendars are +climatological, so the 2015 timing is valid for the 2020 baseline areas. + +Growing-area months are not requirement months, however: a crop's net +irrigation requirement is modulated by evapotranspiration minus effective +precipitation within its season (it collapses during the monsoon and peaks in +the dry shoulder months), while the growing-area profile weights every month +of the season equally -- including dormant winter-wheat months and rain-fed +monsoon months. WaterGAP's ``pirruse`` carries the requirement timing on the +same simulation and basis as the supply envelope. The two are combined by +iterative proportional fitting (``retime_shares_to_demand``): per region, the +crop x month prior (MIRCA area shares weighted by each crop's annual +irrigation water) is scaled so that region-month column totals match the +WaterGAP monthly requirement shape while per-crop annual totals and the +structural zeros of the observed calendar are preserved exactly. Wheat can +shift within its rabi window but never into the monsoon. + +Sub-crops of the same class (``Wheat1`` + ``Wheat2``, ``Rice1..3``) are +summed: all cycles of a crop feed the same regional water buses, so only the +summed monthly profile matters. The summed grids are aggregated to model +regions (coverage-weighted, exact_extract) and normalised into monthly +shares. Only irrigated grids are used -- rainfed links carry no water. +A calendar-only supplement mapping adds MIRCA classes excluded from the +multi-cropping concordance (sugar cane, pulses, fodder) so that the large +irrigators they represent are placed by observed timing instead of the flat +GAEZ fallback; a class mapping to several GLADE crops gives each the same +profile with the class area split evenly for weighting. + +Output ``mirca_crop_calendar.csv``: ``region, crop, month, share, area_ha`` +with one row per (region, mapped GLADE crop, month) where the region grows +the crop under irrigation in MIRCA-OS. ``share`` is the retimed demand share +(summing to 1 over the year); ``area_ha`` is the raw MIRCA-OS growing area +(provenance, not rescaled). Consumers (single-crop links in +``build_model.crops`` and the multi-cropping cycle split in +``build_multi_cropping``) fall back to the GAEZ growing season where a +(region, crop) is absent. +""" + +import logging +from pathlib import Path + +from exactextract import exact_extract +from exactextract.raster import NumPyRasterSource +import geopandas as gpd +import numpy as np +import pandas as pd +import xarray as xr + +logger = logging.getLogger(__name__) + +OUTPUT_COLUMNS = ["region", "crop", "month", "share", "area_ha"] + +# exactextract's NumPyRasterSource retains, at the C++ level, the numpy array it +# is handed, and that memory is never reclaimed even after the source is GC'd. +# Because this module wraps a fresh full-grid stack for every MIRCA crop, that +# leak would accumulate to many GB. Reusing a single buffer per grid shape means +# only one array is ever retained -- copying the transient stack into the buffer +# lets the transient be freed normally. +_EXTRACT_BUFFERS: dict[tuple[int, ...], np.ndarray] = {} + + +def get_extract_buffer(shape: tuple[int, ...]) -> np.ndarray: + """Return the reused float64 exact_extract buffer for ``shape``.""" + buffer = _EXTRACT_BUFFERS.get(shape) + if buffer is None: + buffer = np.empty(shape, dtype=np.float64) + _EXTRACT_BUFFERS[shape] = buffer + return buffer + + +def aggregate_raster_stack_by_region( + stack: np.ndarray, + regions_gdf: gpd.GeoDataFrame, + xmin: float, + ymin: float, + xmax: float, + ymax: float, + crs_wkt: str | None, + stat: str = "sum", +) -> pd.DataFrame: + """Aggregate a (band, H, W) raster stack by regions in one exact_extract call. + + All bands share one grid, so exact_extract computes each region's cell + coverage fractions once and reuses them across bands -- roughly a + band-count speedup over per-band calls. The stack is copied into a reused + per-shape buffer (see ``_EXTRACT_BUFFERS``). + + Returns a DataFrame with ``region`` and one ``b{i}_{stat}`` column per band. + """ + buffer = get_extract_buffer(stack.shape) + if stack is not buffer: + buffer[:] = stack + sources = [ + NumPyRasterSource( + buffer[i], + xmin=xmin, + ymin=ymin, + xmax=xmax, + ymax=ymax, + nodata=np.nan, + name=f"b{i}", + srs_wkt=crs_wkt, + ) + for i in range(stack.shape[0]) + ] + return exact_extract( + sources, + regions_gdf, + [stat], + include_cols=["region"], + output="pandas", + ) + + +def load_crop_monthly_area(paths: list[str]) -> tuple[np.ndarray, tuple]: + """Sum a crop's sub-crop monthly growing-area grids into one (12, H, W). + + Returns the summed array (ha per cell-month, NaN treated as zero) and the + grid bounds ``(xmin, ymin, xmax, ymax)``. All grids must share the MIRCA-OS + 5-arcmin north-up layout. The sum is accumulated directly in the shared + exact_extract buffer to avoid holding a second full-grid copy. + """ + total = None + bounds = None + for path in paths: + ds = xr.open_dataset(path) + da = ds["harvested_area"] # (month, latitude, longitude), ha + lat = ds["latitude"].values.astype(float) + lon = ds["longitude"].values.astype(float) + if lat[0] < lat[-1]: + raise ValueError(f"Expected north-up latitude axis in {path}") + arr = np.nan_to_num(da.values, nan=0.0, copy=False) + ds.close() + if total is None: + total = get_extract_buffer(arr.shape) + total[:] = arr + res = float(abs(lon[1] - lon[0])) + bounds = ( + float(lon.min()) - res / 2, + float(lat.min()) - res / 2, + float(lon.max()) + res / 2, + float(lat.max()) + res / 2, + ) + else: + if arr.shape != total.shape: + raise ValueError(f"Grid shape mismatch in {path}") + total += arr + return total, bounds + + +def monthly_shares( + monthly_area: pd.DataFrame, min_area_ha: float = 1.0 +) -> pd.DataFrame: + """Normalise per-(region, crop) monthly areas into demand shares. + + ``monthly_area`` has columns ``region, crop, month, area_ha``. Regions with + an annual total below ``min_area_ha`` are dropped (no reliable calendar + signal); the remaining shares sum to 1 over the 12 months. + """ + annual = monthly_area.groupby(["region", "crop"])["area_ha"].transform("sum") + out = monthly_area[annual >= min_area_ha].copy() + out["share"] = out["area_ha"] / annual[annual >= min_area_ha] + return out[OUTPUT_COLUMNS].sort_values(["crop", "region", "month"]) + + +def water_weights(yield_paths: dict[str, str], crops: set[str]) -> pd.DataFrame: + """Per-(region, crop) annual irrigation water requirement rates (m3/ha). + + Reads the irrigated GAEZ yield tables (``{crop}_i.csv``) for the requested + crops and returns the suitable-area-weighted mean of + ``water_requirement_m3_per_ha`` over resource classes, as a DataFrame with + columns ``region, crop, req_m3_per_ha``. Crops without an irrigated yield + table are absent (they build no irrigated links, so they carry no weight). + """ + frames = [] + for crop in sorted(crops & set(yield_paths)): + df = pd.read_csv(yield_paths[crop]) + wide = df.pivot_table( + index=["region", "resource_class"], columns="variable", values="value" + ) + if {"water_requirement_m3_per_ha", "suitable_area"} - set(wide.columns): + continue + wide = wide.reset_index() + area = wide["suitable_area"].fillna(0.0) + req = wide["water_requirement_m3_per_ha"].fillna(0.0) + grouped = ( + pd.DataFrame({"region": wide["region"], "num": req * area, "den": area}) + .groupby("region") + .sum() + ) + rate = (grouped["num"] / grouped["den"].replace(0.0, np.nan)).dropna() + frames.append(rate.rename("req_m3_per_ha").reset_index().assign(crop=crop)) + if not frames: + return pd.DataFrame(columns=["region", "crop", "req_m3_per_ha"]) + return pd.concat(frames, ignore_index=True) + + +def retime_shares_to_demand( + shares: pd.DataFrame, + weights: pd.Series, + demand: pd.DataFrame, + n_iter: int = 200, + tol: float = 1e-6, + min_demand_mm3: float = 1.0, +) -> pd.DataFrame: + """Retime per-(region, crop) monthly shares to a region-month demand shape. + + Iterative proportional fitting on each region's crop x month demand + matrix: the prior is ``share * weight`` (MIRCA growing-area placement + scaled by each crop's annual irrigation water), the row marginals are the + crop weights (annual totals preserved), and the column marginals are the + region's WaterGAP monthly requirement shape scaled to the same total. + Multiplicative updates preserve structural zeros, so demand moves within + each crop's observed season, never outside it. Months WaterGAP serves that + no crop grows in are left unmet (the column scaling skips empty columns); + the final row normalisation keeps every profile summing to exactly 1. + + ``shares``: columns ``region, crop, month, share`` (full 12-month + profiles). ``weights``: (region, crop)-indexed annual water volumes. + ``demand``: columns ``region, month, irrigation_consumption_mm3``. Rows + without a positive weight, and regions whose annual demand is below + ``min_demand_mm3`` (no reliable requirement signal), pass through + unchanged. Returns ``shares`` with the ``share`` column retimed. + """ + wide = shares.pivot_table( + index=["region", "crop"], columns="month", values="share" + ).reindex(columns=range(1, 13), fill_value=0.0) + + dem = demand.pivot_table( + index="region", columns="month", values="irrigation_consumption_mm3" + ).reindex(columns=range(1, 13), fill_value=0.0) + dem_total = dem.sum(axis=1) + dem_shape = dem[dem_total >= min_demand_mm3].div( + dem_total[dem_total >= min_demand_mm3], axis=0 + ) + + w = weights.reindex(wide.index).fillna(0.0) + regions = wide.index.get_level_values("region") + active = (w > 0) & regions.isin(dem_shape.index) + if not active.any(): + return shares + + prior = wide[active].mul(w[active], axis=0) + w_act = w[active] + reg_act = prior.index.get_level_values("region") + # Column targets: the region's demand shape scaled to the total prior + # weight of the participating crops. + totals = w_act.groupby(reg_act).sum() + target = dem_shape.reindex(totals.index).mul(totals, axis=0) + target_rows = target.loc[reg_act].set_axis(prior.index) + + for _ in range(n_iter): + colsum = prior.groupby(reg_act).transform("sum") + prior *= (target_rows / colsum).where(colsum > 0, 1.0).to_numpy() + rowsum = prior.sum(axis=1) + prior = prior.mul((w_act / rowsum).where(rowsum > 0, 1.0), axis=0) + col_err = (prior.groupby(reg_act).sum() - target).abs().to_numpy() + if col_err.max() <= tol * max(totals.max(), 1.0): + break + + # Profiles whose season does not intersect the region's demand months are + # zeroed entirely by the column scaling (structurally infeasible to + # retime); they keep their original observed shares. + rowsum = prior.sum(axis=1) + retimed = prior.div(rowsum.where(rowsum > 0, 1.0), axis=0) + dead = rowsum <= 0 + if dead.any(): + retimed[dead] = wide[active][dead] + logger.info( + "%d (region, crop) profiles do not overlap the demand months; " + "kept their observed shares", + int(dead.sum()), + ) + wide.loc[active] = retimed + + out = shares.copy() + lookup = wide.stack().rename("share") + out["share"] = lookup.reindex( + pd.MultiIndex.from_arrays( + [out["region"], out["crop"], out["month"]], + names=["region", "crop", "month"], + ) + ).to_numpy() + if out["share"].isna().any(): + raise ValueError("Retiming lost shares for some (region, crop, month) rows") + return out + + +if __name__ == "__main__": + inp = dict(snakemake.input.items()) # type: ignore[name-defined] + regions_path = inp.pop("regions") + mapping_path = inp.pop("mapping") + supplement_path = inp.pop("supplement") + demand_path = inp.pop("demand") + yield_files = snakemake.input.crop_yields # type: ignore[name-defined] + inp.pop("crop_yields", None) + output = Path(snakemake.output[0]) # type: ignore[name-defined] + + # Remaining inputs are the monthly irrigated grids, keyed "nc_{label}" + # (label = MIRCA base crop + optional sub-crop digit, e.g. "nc_Wheat1"). + nc_paths = { + key[len("nc_") :]: path for key, path in inp.items() if key.startswith("nc_") + } + + mapping = pd.concat( + [ + pd.read_csv(mapping_path, comment="#"), + pd.read_csv(supplement_path, comment="#"), + ], + ignore_index=True, + ) + mapping = mapping[mapping["glade_crop"].notna() & (mapping["glade_crop"] != "")] + # A MIRCA class mapping to several GLADE crops splits its area evenly + # between them for weighting (the monthly profile is shared). + n_targets = mapping.groupby("mirca_crop")["glade_crop"].transform("count") + + # Group sub-crop labels under their MIRCA base crop by stripping the + # trailing cycle digit ("Wheat1" -> "Wheat"). + label_base = {label: label.rstrip("0123456789").strip() for label in nc_paths} + + regions_gdf = gpd.read_file(regions_path)[["region", "geometry"]].reset_index() + + base_area: dict[str, pd.DataFrame] = {} + records = [] + for row, n_tgt in zip(mapping.itertuples(), n_targets, strict=True): + labels = [lb for lb, base in label_base.items() if base == row.mirca_crop] + if not labels: + raise KeyError( + f"No monthly MIRCA-OS grids supplied for mapped crop " + f"'{row.mirca_crop}' (GLADE '{row.glade_crop}')" + ) + if row.mirca_crop not in base_area: + monthly, (xmin, ymin, xmax, ymax) = load_crop_monthly_area( + [nc_paths[lb] for lb in labels] + ) + stats = aggregate_raster_stack_by_region( + monthly, regions_gdf, xmin, ymin, xmax, ymax, None + ) + long = stats.melt(id_vars="region", var_name="band", value_name="area_ha") + long["month"] = ( + long["band"].str.extract(r"b(\d+)_", expand=False).astype(int) + 1 + ) + long = long[long["area_ha"].fillna(0.0) > 0.0] + base_area[row.mirca_crop] = long[["region", "month", "area_ha"]] + logger.info( + "Aggregated MIRCA-OS 2015 irrigated calendar for %s (<- %s)", + row.mirca_crop, + " + ".join(sorted(labels)), + ) + crop_frame = base_area[row.mirca_crop].assign( + crop=row.glade_crop, weight_divisor=float(n_tgt) + ) + records.append( + crop_frame[["region", "crop", "month", "area_ha", "weight_divisor"]] + ) + + monthly_area = ( + pd.concat(records, ignore_index=True) + if records + else pd.DataFrame( + columns=["region", "crop", "month", "area_ha", "weight_divisor"] + ) + ) + # Complete each (region, crop) to all 12 months (zero growing area in the + # missing months) so shares are a full monthly profile. + if not monthly_area.empty: + divisor = monthly_area.groupby(["region", "crop"])["weight_divisor"].first() + full = ( + monthly_area.set_index(["region", "crop", "month"])["area_ha"] + .unstack("month") + .reindex(columns=range(1, 13), fill_value=0.0) + .fillna(0.0) + .stack() + .rename("area_ha") + .reset_index() + ) + shares = monthly_shares(full) + + # Retime the growing-area placement to WaterGAP's monthly requirement + # shape: weight each crop by its annual irrigation water (MIRCA area x + # GAEZ requirement rate, class area split between shared targets). + req = water_weights( + { + Path(p).stem[: -len("_i")]: p + for p in yield_files + if Path(p).stem.endswith("_i") + }, + set(shares["crop"].unique()), + ) + annual_area = full.groupby(["region", "crop"])["area_ha"].sum() + req_rate = req.set_index(["region", "crop"])["req_m3_per_ha"] + weights = (annual_area * req_rate.reindex(annual_area.index)).fillna( + 0.0 + ) / divisor.reindex(annual_area.index).fillna(1.0) + + demand = pd.read_csv(demand_path) + shares = retime_shares_to_demand(shares, weights, demand) + logger.info( + "Retimed %d (region, crop) profiles (of %d) to the WaterGAP " + "requirement shape", + int((weights > 0).sum()), + len(annual_area), + ) + else: + shares = pd.DataFrame(columns=OUTPUT_COLUMNS) + + output.parent.mkdir(parents=True, exist_ok=True) + shares[OUTPUT_COLUMNS].to_csv(output, index=False) diff --git a/workflow/scripts/build_model.py b/workflow/scripts/build_model.py index ada0e82b..edc95471 100644 --- a/workflow/scripts/build_model.py +++ b/workflow/scripts/build_model.py @@ -24,6 +24,7 @@ grassland, health, infrastructure, + irrigation, land, nutrition, primary_resources, @@ -372,6 +373,7 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: multi_cropping_area_df = read_csv(snakemake.input.multi_cropping_area) multi_cropping_cycle_df = read_csv(snakemake.input.multi_cropping_yields) multi_cropping_baseline_df = read_csv(snakemake.input.multi_cropping_baseline) + irrigation_calendar_df = read_csv(snakemake.input.mirca_crop_calendar) luc_lef_lookup = pd.DataFrame( columns=["region", "resource_class", "water_supply", "use", "lef"] @@ -515,21 +517,16 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: "Grazing-only land data is available but current grassland area is empty" ) - blue_water_availability_df = read_csv(snakemake.input.blue_water_availability) - monthly_region_water_df = read_csv(snakemake.input.monthly_region_water) - region_growing_water_df = read_csv(snakemake.input.growing_season_water) + water_tiers_df = read_csv(snakemake.input.water_tiers) + groundwater_bands_df = read_csv(snakemake.input.groundwater_bands) + region_agri_consumption = read_csv( + snakemake.input.region_agri_consumption + ).set_index("region")["agri_consumption_m3"] logger.info( - "Loaded blue water availability data: %d basin-month pairs", - len(blue_water_availability_df), - ) - logger.info( - "Loaded monthly region water availability: %d rows", - len(monthly_region_water_df), - ) - logger.info( - "Loaded region growing-season water availability: %d regions", - region_growing_water_df.shape[0], + "Loaded water supply: %d region-period surface tiers, %d groundwater bands", + len(water_tiers_df), + len(groundwater_bands_df), ) # Load population per country for planning horizon @@ -560,23 +557,19 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: regions = sorted(region_to_country.index.unique()) - region_water_limits = ( - region_growing_water_df.set_index("region")["growing_season_water_available_m3"] - .reindex(regions) - .fillna(0.0) - ) - + # Regions that actually draw irrigation water: those with positive irrigated + # suitable area (the same condition under which crops.py builds an irrigated + # produce link). Regions present in the yield tables with zero suitable area + # get no produce link, so they must not get a water bus -- a dangling bus + # with no attached components breaks the LP during matrix assembly. irrigated_regions: set[str] = set() for key, df in yields_data.items(): if key.endswith("_yield_i"): - irrigated_regions.update(df.index.get_level_values("region")) + positive = df[df["suitable_area"] > 0] + irrigated_regions.update(positive.index.get_level_values("region")) land_regions = set(land_class_df.index.get_level_values("region")) - water_bus_regions = sorted( - set(region_water_limits.index) - .union(irrigated_regions) - .intersection(land_regions) - ) + water_bus_regions = sorted(irrigated_regions & land_regions) logger.debug("Foods data:\n%s", foods.head()) logger.debug("Food groups data:\n%s", food_groups.head()) @@ -738,6 +731,9 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: # All nutrients from nutrition data get buses (tracked but not necessarily constrained) all_nutrient_names = list(nutrient_units.keys()) + # Number of intra-year water periods the LP resolves (structural). + water_periods = int(snakemake.config["water"]["temporal_resolution"]) + # Infrastructure: carriers and buses infrastructure.add_carriers_and_buses( n, @@ -750,6 +746,7 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: cfg_countries, regions, water_bus_regions, + water_periods, food_basis=food_basis, ) @@ -790,15 +787,25 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: # Primary resources: water, fertilizer, emissions water_slack_cost = validation_slack_cost / 1e3 + groundwater_pumping_cost = float( + snakemake.config["water"]["supply"]["pumping_cost_usd_per_m3"] + ) primary_resources.add_primary_resources( n, snakemake.params.fertilizer, - region_water_limits, + water_tiers_df, + groundwater_bands_df, + water_bus_regions, + water_periods, ch4_to_co2_factor, n2o_to_co2_factor, use_actual_production=use_actual_production, water_slack_cost=water_slack_cost, + groundwater_pumping_cost_usd_per_m3=groundwater_pumping_cost, + min_water_capacity_mm3=float( + snakemake.params.numerics["min_water_capacity_mm3"] + ), ) synthetic_n2o_factor = float( snakemake.params.emissions["fertilizer"]["synthetic_n2o_factor"] @@ -987,6 +994,8 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: residue_fue_lookup=residue_fue_lookup, residue_n2o_eff_lookup=residue_n2o_eff_lookup, use_actual_production=use_actual_production, + water_periods=water_periods, + irrigation_calendar=irrigation_calendar_df, cost_calibration=crop_cost_calibration, min_yield_t_per_ha=min_crop_yield, seed_kg_dm_per_ha=seed_kg_dm_per_ha, @@ -1018,6 +1027,7 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: residue_lookup, residue_fue_lookup=residue_fue_lookup, residue_n2o_eff_lookup=residue_n2o_eff_lookup, + water_periods=water_periods, rice_methane_factor=rice_methane_factor, rainfed_wetland_rice_ch4_scaling_factor=( rainfed_wetland_rice_ch4_scaling_factor @@ -1074,6 +1084,22 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: land_use_cost_bnusd_per_mha=land_use_cost_bnusd_per_mha, ) + # Irrigation delivery links (after the crop links and the baseline + # reconciliation: eta_c is calibrated from their baseline areas and water + # requirements). + irrigation_cfg = snakemake.config["water"]["irrigation"] + irrigation.add_irrigation_delivery( + n, + region_agri_consumption, + water_tiers_df, + groundwater_bands_df, + water_bus_regions, + water_periods, + eta_min=float(irrigation_cfg["eta_min"]), + eta_max=float(irrigation_cfg["eta_max"]), + consumed_fraction=float(irrigation_cfg["consumed_fraction"]), + ) + if snakemake.params.grazing["enabled"]: grassland.add_grassland_feed_links( n, diff --git a/workflow/scripts/build_model/crops.py b/workflow/scripts/build_model/crops.py index d7215ed9..e8bb4dd0 100644 --- a/workflow/scripts/build_model/crops.py +++ b/workflow/scripts/build_model/crops.py @@ -19,10 +19,17 @@ import pypsa from .. import constants +from ..water_periods import ( + calendar_period_shares, + crop_monthly_shares, + period_demand_shares, +) from .utils import merge_lef logger = logging.getLogger(__name__) +__all__ = ["period_demand_shares"] + # Crops grown under flooded (wetland) conditions, which emit CH4 per harvested # cycle. Kept in sync with the multiple-cropping derivation's own set. WETLAND_RICE_CROPS = {"wetland-rice"} @@ -330,6 +337,8 @@ def add_regional_crop_production_links( residue_n2o_eff_lookup: Mapping[str, float] | None = None, use_actual_production: bool = False, *, + water_periods: int, + irrigation_calendar: pd.DataFrame, cost_calibration: pd.Series | None = None, min_yield_t_per_ha: float, seed_kg_dm_per_ha: pd.Series, @@ -466,14 +475,49 @@ def add_regional_crop_production_links( if df.empty: continue + # Split each irrigated crop's net requirement across the model's + # water periods by the observed MIRCA-OS calendar (falling back to the + # GAEZ growing season where MIRCA has no observation): (len(df), T) + # arrays of per-period field buses and (negative) efficiencies. + # Rainfed links carry no water. Periods with no demand get an empty bus. + region_str = df["region"].astype(str) if ws == "i": - water_bus = ("water:" + df["region"].astype(str)).to_numpy(dtype=object) - water_eff = -pd.to_numeric( + requirement = -pd.to_numeric( df["water_requirement_m3_per_ha"], errors="coerce" ).to_numpy(dtype=float) + nan_col = pd.Series(np.nan, index=df.index) + start = pd.to_numeric( + df["growing_season_start_day"] + if "growing_season_start_day" in df.columns + else nan_col, + errors="coerce", + ).to_numpy(dtype=float) + length = pd.to_numeric( + df["growing_season_length_days"] + if "growing_season_length_days" in df.columns + else nan_col, + errors="coerce", + ).to_numpy(dtype=float) + monthly = crop_monthly_shares( + irrigation_calendar, crop, df["region"].to_numpy() + ) + shares, _ = calendar_period_shares( + monthly, start, length, water_periods + ) # (N, T) + water_eff_periods = requirement[:, None] * shares + period_bus = [ + ("water_field:" + region_str + f":p{p}").to_numpy(dtype=object) + for p in range(water_periods) + ] + water_bus_periods = [ + np.where(water_eff_periods[:, p] != 0.0, period_bus[p], "") + for p in range(water_periods) + ] else: - water_bus = np.full(len(df), "", dtype=object) - water_eff = np.zeros(len(df), dtype=float) + water_eff_periods = np.zeros((len(df), water_periods), dtype=float) + water_bus_periods = [ + np.full(len(df), "", dtype=object) for _ in range(water_periods) + ] if crop in WETLAND_RICE_CROPS and rice_methane_factor > 0: scaling_factor = ( @@ -537,12 +581,20 @@ def add_regional_crop_production_links( dtype=float ) row_df["baseline_area_mha"] = ha / constants.HA_PER_MHA - row_df["bus2"] = water_bus - row_df["efficiency2"] = water_eff - row_df["bus3"] = ("fertilizer:" + df["country"].astype(str)).to_numpy() - row_df["efficiency3"] = fert_efficiency - row_df["bus4"] = ch4_bus - row_df["efficiency4"] = ch4_eff + # Water occupies a fixed block bus2 .. bus(1 + T); fertilizer, CH4, + # (residue and N2O below) follow at the shifted offsets. At T = 1 this + # is exactly the historical bus2/3/4/5/6 layout. + for p in range(water_periods): + row_df[f"bus{2 + p}"] = water_bus_periods[p] + row_df[f"efficiency{2 + p}"] = water_eff_periods[:, p] + fert_i = 2 + water_periods + ch4_i = fert_i + 1 + row_df[f"bus{fert_i}"] = ( + "fertilizer:" + df["country"].astype(str) + ).to_numpy() + row_df[f"efficiency{fert_i}"] = fert_efficiency + row_df[f"bus{ch4_i}"] = ch4_bus + row_df[f"efficiency{ch4_i}"] = ch4_eff row_df["harvested_area_ha"] = ha row_df["p_nom_max"] = ( pd.to_numeric(df["suitable_area"], errors="coerce").to_numpy( @@ -644,15 +696,19 @@ def add_regional_crop_production_links( if residue_eff6[i] > 0.0: has_residue_n2o = True - all_df["bus5"] = residue_bus5 - # efficiency5 is the NET residue yield (gross * FUE) on the feed-usable + # Residue (net feed-usable DM) and mandatory soil-N2O buses follow the water + # block (bus2 .. bus(1 + T)), fertilizer (bus(2 + T)) and CH4 (bus(3 + T)). + residue_i = 4 + water_periods + n2o_i = 5 + water_periods + all_df[f"bus{residue_i}"] = residue_bus5 + # efficiency is the NET residue yield (gross * FUE) on the feed-usable # residue bus. Don't scale by loss_mults: the crop bus carries post- # loss product, but residues stay in the field and don't share the # storage / transport / processing loss path of the grain. - all_df["efficiency5"] = residue_eff5 + all_df[f"efficiency{residue_i}"] = residue_eff5 if has_residue_n2o: - all_df["bus6"] = np.where(residue_eff6 > 0.0, "emission:n2o", "") - all_df["efficiency6"] = residue_eff6 + all_df[f"bus{n2o_i}"] = np.where(residue_eff6 > 0.0, "emission:n2o", "") + all_df[f"efficiency{n2o_i}"] = residue_eff6 add_kwargs: dict[str, object] = { "carrier": "crop_production", @@ -673,8 +729,9 @@ def add_regional_crop_production_links( "bounded_subsidy_bnusd_per_mha": all_df["bounded_subsidy_bnusd_per_mha"], "bounded_penalty_bnusd_per_mha": all_df["bounded_penalty_bnusd_per_mha"], } - # Multi-input buses: water, fertilizer, CH4, residue and (optional) soil-N2O. - for i in range(2, 7): + # Multi-input buses: the per-period water block, fertilizer, CH4, residue and + # (optional) soil-N2O, at the T-dependent offsets assigned above. + for i in range(2, 6 + water_periods): bus_col = f"bus{i}" if bus_col in all_df.columns: add_kwargs[bus_col] = all_df[bus_col] @@ -696,6 +753,7 @@ def add_multi_cropping_links( residue_fue_lookup: Mapping[str, float] | None = None, residue_n2o_eff_lookup: Mapping[str, float] | None = None, *, + water_periods: int, rice_methane_factor: float, rainfed_wetland_rice_ch4_scaling_factor: float, min_yield_t_per_ha: float, @@ -845,13 +903,7 @@ def add_multi_cropping_links( area_df = eligible_area.copy() if area_df.empty: - area_df = pd.DataFrame( - columns=[ - *key_cols, - "eligible_area_ha", - "water_requirement_m3_per_ha", - ] - ) + area_df = pd.DataFrame(columns=[*key_cols, "eligible_area_ha"]) area_df["combination"] = area_df["combination"].astype(str) area_df = area_df[area_df["combination"].isin(configured)] area_df["resource_class"] = area_df["resource_class"].astype(int) @@ -859,9 +911,12 @@ def add_multi_cropping_links( area_df["eligible_area_ha"] = pd.to_numeric( area_df["eligible_area_ha"], errors="coerce" ) - area_df["water_requirement_m3_per_ha"] = pd.to_numeric( - area_df.get("water_requirement_m3_per_ha", 0.0), errors="coerce" - ).fillna(0.0) + # Per-period irrigation requirement (m3/ha), one column per intra-year water + # period, split by each cycle's observed calendar in build_multi_cropping. + # Missing columns default to 0 (e.g. rainfed-only combination sets). + water_cols = [f"water_requirement_m3_per_ha_p{p}" for p in range(water_periods)] + for col in water_cols: + area_df[col] = pd.to_numeric(area_df.get(col, 0.0), errors="coerce").fillna(0.0) region_to_country = region_to_country.astype(str) area_df["country"] = area_df["region"].map(region_to_country) @@ -889,7 +944,7 @@ def add_multi_cropping_links( ) valid_land = area_df["land_bus"].isin(n.buses.static.index) valid_water = ~area_df["water_supply"].eq("i") | ( - area_df["water_requirement_m3_per_ha"] > 0 + area_df[water_cols].sum(axis=1) > 0 ) n_invalid = int((~(valid_land & valid_water)).sum()) if n_invalid: @@ -1010,7 +1065,7 @@ def add_multi_cropping_links( [ *key_cols, "eligible_area_ha", - "water_requirement_m3_per_ha", + *water_cols, "country", ], ] @@ -1283,11 +1338,12 @@ def add_multi_cropping_links( n.carriers.add("crop_production_multi", unit="Mha") def _water_gate(df): - """Validity/invalidity masks for the per-link water requirement (m3/ha).""" - total = df["water_requirement_m3_per_ha"].to_numpy(dtype=float) + """Validity/invalidity masks and total per-link requirement (m3/ha).""" + period_arr = df[water_cols].to_numpy(dtype=float) # (N, T) + total = np.nansum(period_arr, axis=1) irrigated = df["water_supply"].eq("i").to_numpy() valid = irrigated & (total > 0) - # An irrigated combination with no positive water requirement is a + # An irrigated combination with no positive per-period requirement is a # data-quality hole (build_multi_cropping zero-fills empty water # aggregations): the link would otherwise be built with the irrigated # per-cycle yields but no water port, i.e. free irrigation. Drop it. @@ -1311,16 +1367,17 @@ def _water_gate(df): return water_valid, _ = _water_gate(index_df) - # bus0 is land in Mha, the water bus is Mm3, so the coefficient is - # m3/ha (numerically equal to Mm3/Mha). water_requirement_m3_per_ha is - # already in m3/ha after build_multi_cropping converts the GAEZ mm raster. - # Water is an input, hence the negative sign. - index_df["water_efficiency"] = np.where( - water_valid, - -index_df["water_requirement_m3_per_ha"].to_numpy(dtype=float), - 0.0, - ) - index_df["has_water"] = water_valid.astype(int) + # bus0 is land in Mha, water buses are Mm3, so each period coefficient is + # m3/ha (numerically equal to Mm3/Mha). The per-period columns already carry + # each cycle's calendar-placed requirement, so each period's own requirement + # is wired onto its water port. Water is an input, hence the negative sign. + # All T slots stay reserved (has_water = T) even where a period's demand is + # 0, so downstream port offsets are stable. + for period, col in enumerate(water_cols): + index_df[f"water_eff_p{period}"] = np.where( + water_valid, -index_df[col].to_numpy(dtype=float), 0.0 + ) + index_df["has_water"] = np.where(water_valid, int(water_periods), 0) fert_total = index_df["fertilizer_total"].astype(float) fert_valid = fert_total > 0 @@ -1387,25 +1444,27 @@ def _water_gate(df): entry_frames = [outputs_entries] - water_columns = [*key_cols, "link_name", "water_efficiency", "crop_count"] - water_entries = index_df.loc[index_df["has_water"] == 1, water_columns].copy() - if not water_entries.empty: - water_entries["offset"] = water_entries["crop_count"] + 1 - offset_str = water_entries["offset"].astype(int).astype(str) - water_entries["bus_col"] = "bus" + offset_str - water_entries["eff_col"] = "efficiency" + offset_str - water_entries.loc[water_entries["offset"].eq(1), "eff_col"] = "efficiency" - water_entries["bus_value"] = "water:" + water_entries["region"].astype(str) - water_entries = water_entries[ - [ - "link_name", - "bus_col", - "bus_value", - "eff_col", - "water_efficiency", - ] - ].rename(columns={"water_efficiency": "eff_value"}) - entry_frames.append(water_entries) + water_eff_cols = [f"water_eff_p{p}" for p in range(water_periods)] + water_columns = [*key_cols, "link_name", "crop_count", *water_eff_cols] + water_src = index_df.loc[index_df["has_water"] > 0, water_columns].copy() + if not water_src.empty: + # One water entry per period: buses at offsets crop_count+1 .. crop_count+T + # (right after the crop outputs), each carrying its own calendar-placed + # requirement. A zero-demand period keeps its reserved port. + period_frames = [] + for p in range(water_periods): + w = water_src.copy() + w["offset"] = w["crop_count"] + 1 + p + offset_str = w["offset"].astype(int).astype(str) + w["bus_col"] = "bus" + offset_str + w["eff_col"] = "efficiency" + offset_str + w.loc[w["offset"].eq(1), "eff_col"] = "efficiency" + w["bus_value"] = "water_field:" + w["region"].astype(str) + f":p{p}" + w["eff_value"] = w[f"water_eff_p{p}"] + period_frames.append( + w[["link_name", "bus_col", "bus_value", "eff_col", "eff_value"]] + ) + entry_frames.append(pd.concat(period_frames, ignore_index=True)) fert_entries = index_df[index_df["has_fertilizer"] == 1][ [ diff --git a/workflow/scripts/build_model/infrastructure.py b/workflow/scripts/build_model/infrastructure.py index 1e996303..ea7b9694 100644 --- a/workflow/scripts/build_model/infrastructure.py +++ b/workflow/scripts/build_model/infrastructure.py @@ -8,6 +8,7 @@ foundation of the PyPSA network model. """ +import numpy as np import pandas as pd import pypsa @@ -26,6 +27,7 @@ def add_carriers_and_buses( countries: list, regions: list, water_regions: list, + water_periods: int, food_basis: dict[str, str], ) -> None: """Add all carriers and their corresponding buses to the network. @@ -191,8 +193,14 @@ def add_carriers_and_buses( n.carriers.add("feed_conversion", unit="Mt") - # Water carrier (buses added per region below) + # Water carrier (buses added per region below). The global water source feeds + # the tiered regional supply links; water scarcity accumulates on a global bus. n.carriers.add("water", unit="Mm^3") + n.carriers.add("water_field", unit="Mm^3") + n.carriers.add("water_source", unit="Mm^3") + n.carriers.add("water_scarcity", unit="Mm^3 world-eq") + n.carriers.add("groundwater_depletion", unit="Mm^3 mined") + n.carriers.add("groundwater_renewable", unit="Mm^3") # Global emission and resource carriers with buses for carrier, unit in [ @@ -203,7 +211,8 @@ def add_carriers_and_buses( ("ghg", "MtCO2e"), ]: n.carriers.add(carrier, unit=unit) - # Add global emission buses and fertilizer supply bus + # Add global emission buses, fertilizer supply bus, water source and water + # scarcity buses n.buses.add( [ "emission:co2", @@ -211,8 +220,22 @@ def add_carriers_and_buses( "emission:n2o", "emission:ghg", "fertilizer:supply", + "water:source", + "impact:water_scarcity", + "impact:groundwater_depletion", + "impact:groundwater_renewable", + ], + carrier=[ + "co2", + "ch4", + "n2o", + "ghg", + "fertilizer", + "water_source", + "water_scarcity", + "groundwater_depletion", + "groundwater_renewable", ], - carrier=["co2", "ch4", "n2o", "ghg", "fertilizer"], ) # Per-country fertilizer buses @@ -239,6 +262,22 @@ def add_carriers_and_buses( ): scale_meta["macronutrient_kcal_to_PJ"] = constants.KCAL_TO_PJ - # Per-region water buses - water_bus_names = [f"water:{region}" for region in water_regions] - n.buses.add(water_bus_names, carrier="water", region=list(water_regions)) + # Per-region, per-period water buses: the consumption-basis pool (tiered + # supply, scarcity, groundwater bands) and the field bus crops draw their + # beneficial ET from, bridged by the irrigation delivery link. The year is + # split into ``water_periods`` equal periods (water.temporal_resolution) so + # a period's surface availability binds against the crop demand whose growing + # season falls in it; a seasonal shortfall then draws groundwater. + n_periods = int(water_periods) + region_arr = np.array(list(water_regions), dtype=object) + region_col = pd.Series(np.repeat(region_arr, n_periods)) + period_col = pd.Series(np.tile(np.arange(n_periods), len(region_arr))).astype(str) + suffix = region_col.astype(str) + ":p" + period_col + n.buses.add( + ("water:" + suffix).to_numpy(), carrier="water", region=region_col.to_numpy() + ) + n.buses.add( + ("water_field:" + suffix).to_numpy(), + carrier="water_field", + region=region_col.to_numpy(), + ) diff --git a/workflow/scripts/build_model/irrigation.py b/workflow/scripts/build_model/irrigation.py new file mode 100644 index 00000000..a4bbf263 --- /dev/null +++ b/workflow/scripts/build_model/irrigation.py @@ -0,0 +1,213 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: GPL-3.0-or-later + +"""Consumption-basis irrigation delivery. + +The regional water pool (tiered supply, AWARE scarcity, groundwater bands) is +on a *consumption* basis C -- the basin's net water loss, AWARE's own basis -- +while crops need their beneficial evapotranspiration E (the GAEZ net irrigation +requirement). A per-region delivery link bridges the two: + + [water:{region}] --(irrigate:{region}, efficiency = eta_c)--> + [water_field:{region}] <--(crop links, -E) + +The link draws ``C = E / eta_c`` from the pool per unit E delivered; the +``(1 - eta_c)`` difference is non-beneficial consumption (canal and soil +evaporation that genuinely leaves the basin as vapour) and vanishes. Return +flow (withdrawal minus consumption, reused downstream in reality) is handled +implicitly: on a consumption basis it is simply never withdrawn. + +``eta_c`` is calibrated per region so the baseline reproduces observed +consumption: ``eta_c = E_baseline / C``, with ``E_baseline`` the model's own +baseline irrigated areas times net requirements (summed over crop-production +links) and ``C`` the observed irrigation-consumption anchor (WaterGAP +``pirruse``, the same simulation and window as the supply envelope). The +ratio is clipped +to ``[eta_min, eta_max]`` and floored at ``E_baseline / pool`` so the +calibrated baseline draw never exceeds the region's pool (in overexploited +basins the pool is clipped below observed consumption); regions where the +floor or clip binds are logged as diagnostics. + +Values of ``eta_c`` above 1 encode **deficit irrigation**: where the GAEZ +full net requirement exceeds the observed consumption (India, Pakistan, +Thailand, Sudan), real irrigation delivers less water than the +yield-maximising requirement, so one unit of pool consumption satisfies more +than one unit of nominal requirement. Capping at 1 instead would force the +model to draw the full GAEZ requirement and mine the difference. ``eta_max`` +bounds the ratio where the anchor is unreliably small (tiny-consumption +regions), which would otherwise make marginal irrigation nearly free. +""" + +import logging + +import numpy as np +import pandas as pd +import pypsa + +from .. import constants + +logger = logging.getLogger(__name__) + + +def calibrate_eta_c( + e_baseline_mm3: pd.Series, + agri_consumption_mm3: pd.Series, + pool_mm3: pd.Series, + eta_min: float, + eta_max: float, +) -> pd.Series: + """Per-region consumptive irrigation efficiency ``eta_c`` in ``(0, eta_max]``. + + ``eta_c = clip(E_baseline / C, eta_min, eta_max)``, floored at + ``E_baseline / pool`` for baseline feasibility. Values above 1 encode + deficit irrigation (observed consumption below the GAEZ full requirement); + see the module docstring. Regions without baseline irrigation (E = 0) or + without observed consumption (C = 0) get 1.0 (no adjustment: the delivery + link is then pass-through). + """ + regions = e_baseline_mm3.index + e = e_baseline_mm3.to_numpy(dtype=float) + c = agri_consumption_mm3.reindex(regions).fillna(0.0).to_numpy(dtype=float) + pool = pool_mm3.reindex(regions).fillna(0.0).to_numpy(dtype=float) + + with np.errstate(divide="ignore", invalid="ignore"): + raw = np.where((e > 0) & (c > 0), e / np.where(c > 0, c, 1.0), 1.0) + floor = np.where((e > 0) & (pool > 0), e / np.where(pool > 0, pool, 1.0), 0.0) + + clipped_low = (raw < eta_min) & (e > 0) + clipped_high = (raw > eta_max) & (e > 0) + deficit = (raw > 1.0) & (e > 0) + eta = np.clip(raw, eta_min, eta_max) + floored = floor > eta + eta = np.minimum(np.maximum(eta, floor), eta_max) + + infeasible = (e > 0) & (e > eta_max * pool) + if clipped_low.any(): + logger.info( + "eta_c clipped up to eta_min=%.2f in %d regions (E/C below the " + "physical range; data-quality flag): %s", + eta_min, + int(clipped_low.sum()), + ", ".join(regions[clipped_low][:10]), + ) + if deficit.any(): + logger.info( + "eta_c above 1 (deficit irrigation: observed consumption below the " + "GAEZ requirement) in %d regions; E-weighted mean ratio %.2f, " + "capped at eta_max=%.1f in %d regions", + int(deficit.sum()), + float(e[deficit].sum() / c[deficit].sum()), + eta_max, + int(clipped_high.sum()), + ) + if floored.any(): + logger.info( + "eta_c floored at E_baseline/pool in %d regions (pool clipped below " + "observed consumption; overexploited basins): %s", + int(floored.sum()), + ", ".join(regions[floored][:10]), + ) + if infeasible.any(): + logger.warning( + "Baseline irrigation requirement E exceeds the water pool even at " + "eta_c=eta_max in %d regions (baseline-infeasible; expect slack or " + "reallocation there): %s", + int(infeasible.sum()), + ", ".join(regions[infeasible][:10]), + ) + return pd.Series(eta, index=regions, name="eta_c") + + +def add_irrigation_delivery( + n: pypsa.Network, + agri_consumption_m3: pd.Series, + water_tiers: pd.DataFrame, + groundwater_bands: pd.DataFrame, + water_regions: list[str], + water_periods: int, + eta_min: float, + eta_max: float, + consumed_fraction: float, +) -> None: + """Add per-region, per-period ``irrigate:{region}:p{p}`` delivery links. + + Must run after the crop-production links are added: ``E_baseline`` is + computed from their ``baseline_area_mha`` and water coefficients. Water sits + on the per-period buses ``bus2 .. bus(1 + T)`` (efficiency = -requirement + share, m3/ha); summing them recovers the crop's full requirement. One + delivery link is added per (region, period), each converting pool + consumption on ``water:{region}:p{p}`` to beneficial ET on + ``water_field:{region}:p{p}`` at the region's calibrated ``eta_c``. The + ``consumed_fraction`` (C/W) is stashed in ``n.meta`` for the analysis. + """ + regions = pd.Index(sorted(water_regions), dtype="object") + if regions.empty: + return + + # E_baseline per region: baseline irrigated area x net requirement, summed + # over all period water buses on the crop-production links (single-crop AND + # multi-cropping: once irrigated baseline area moves onto multi links, omitting + # them would under-count E_baseline in double-cropped regions and mis-calibrate + # eta_c there). The per-port scan already handles the multi links' T-period + # water block. The Mha * m3/ha product is in Mm3 (design 6.4). + links = n.links.static + crop = links[links["carrier"].isin(["crop_production", "crop_production_multi"])] + water_eff_total = pd.Series(0.0, index=crop.index) + bus_cols = [c for c in crop.columns if c.startswith("bus") and c[3:].isdigit()] + for bus_col in bus_cols: + eff_col = "efficiency" + bus_col[3:] + if eff_col not in crop.columns: + continue + is_field = crop[bus_col].astype(str).str.startswith("water_field:") + water_eff_total = water_eff_total.add( + crop[eff_col].where(is_field, 0.0), fill_value=0.0 + ) + e_baseline = ( + (crop["baseline_area_mha"].fillna(0.0) * (-water_eff_total)) + .groupby(crop["region"]) + .sum() + .reindex(regions) + .fillna(0.0) + ) + + # Total available water per region = surface tiers + annual groundwater bands + # (both feed the region's water buses). The eta_c floor E_baseline/pool uses + # this so the calibrated baseline draw fits the full envelope, not just the + # now-tightened surface. + surface_pool = water_tiers.groupby("region")["capacity_mm3"].sum() + gw_pool = groundwater_bands.groupby("region")["capacity_mm3"].sum() + pool = surface_pool.add(gw_pool, fill_value=0.0) + agri_mm3 = agri_consumption_m3 * constants.MM3_PER_M3 + eta_c = calibrate_eta_c(e_baseline, agri_mm3, pool, eta_min, eta_max) + + # One delivery link per (region, period). Built vectorised: regions repeated + # T-fold, periods tiled, then string-concatenated into the bus/link names. + n_periods = int(water_periods) + region_col = pd.Series(np.repeat(regions.to_numpy(), n_periods)) + period_col = pd.Series(np.tile(np.arange(n_periods), len(regions))).astype(str) + suffix = region_col.astype(str) + ":p" + period_col + + n.carriers.add("irrigation_delivery", unit="Mm^3") + n.links.add( + ("irrigate:" + suffix).to_numpy(), + bus0=("water:" + suffix).to_numpy(), + bus1=("water_field:" + suffix).to_numpy(), + carrier="irrigation_delivery", + efficiency=region_col.map(eta_c).to_numpy(), + p_nom_extendable=True, + region=region_col.to_numpy(), + ) + + n.meta["water_consumed_fraction"] = float(consumed_fraction) + logger.info( + "Added %d irrigation delivery links (%d regions x %d periods); global " + "E_baseline %.0f Mm3, implied baseline consumption %.0f Mm3 " + "(consumption anchor %.0f Mm3)", + len(suffix), + len(regions), + int(water_periods), + float(e_baseline.sum()), + float((e_baseline / eta_c).sum()), + float(agri_mm3.reindex(regions).fillna(0.0).sum()), + ) diff --git a/workflow/scripts/build_model/primary_resources.py b/workflow/scripts/build_model/primary_resources.py index fa90d4f0..ce528e5f 100644 --- a/workflow/scripts/build_model/primary_resources.py +++ b/workflow/scripts/build_model/primary_resources.py @@ -16,6 +16,22 @@ from .. import constants +# Merit-order regularizer for the tiered water supply (bn USD per Mm^3, per unit +# characterisation factor). Breaks the unpriced tier-choice degeneracy by making +# low-CF water marginally cheaper to draw. Sized to be economically negligible +# (equivalent to a ~1e-5 USD/m3-world-eq water price, orders of magnitude below +# the swept water-scarcity prices) yet above solver optimality tolerance. +WATER_MERIT_ORDER_EPSILON = 1e-8 + + +def _retain_material_water_capacities( + capacities: pd.DataFrame, min_capacity_mm3: float +) -> pd.DataFrame: + """Drop supply bands smaller than the configured physical capacity floor.""" + if min_capacity_mm3 == 0: + return capacities + return capacities[capacities["capacity_mm3"] >= min_capacity_mm3].copy() + def _add_land_slack_generators( n: pypsa.Network, bus_names: list[str], marginal_cost: float @@ -44,38 +60,218 @@ def _add_land_slack_generators( def add_primary_resources( n: pypsa.Network, fertilizer_config: dict, - region_water_limits: pd.Series, + water_tiers: pd.DataFrame, + groundwater_bands: pd.DataFrame, + water_regions: Iterable[str], + water_periods: int, ch4_to_co2_factor: float, n2o_to_co2_factor: float, use_actual_production: bool, water_slack_cost: float, + groundwater_pumping_cost_usd_per_m3: float, + min_water_capacity_mm3: float, ) -> None: """Add primary resource components and emissions bookkeeping. - Note: GHG pricing is applied at solve time, not build time. + **Surface water** is supplied through a convex tiered curve: a single free + global source (``water:source``) feeds per-region, per-period, per-tier links + that deliver water to ``water:{region}:p{period}`` (efficiency 1) and + accumulate water scarcity on ``impact:water_scarcity`` at each tier's marginal + characterisation factor (``efficiency2``). The sum of a region-period's tier + capacities is its per-period availability cap (a period's surface draw cannot + exceed it, so a seasonal shortfall must draw groundwater). Capacities are + already in Mm^3. + + **Groundwater** (``groundwater_bands``, groundwater mode only) is instead an + *annual* per-region resource: an aquifer integrates recharge over the year + and can be pumped in any period. Each region gets a ``groundwater:{region}`` + bus fed by two supply links from ``water:source`` -- renewable (WaterGAP + volume, scarcity-priced at the region's scarcest surface CF, tallied on + ``impact:groundwater_renewable``) and non-renewable (a generous ceiling, mined + volume on ``impact:groundwater_depletion``, ordered last by its pumping cost) + -- and free ``groundwater_delivery`` links distribute it to every period bus. + The annual cap therefore lets a dry period draw the whole year's recharge, + unlike surface which is period-bound. + + Because the water itself is free, the unpriced LP is indifferent to which + tier it draws from within a region and lands on an arbitrary (high-CF) mix. + A negligible merit-order regularizer (``marginal_cost = WATER_MERIT_ORDER_ + EPSILON * marginal_cf``) breaks this degeneracy so the LP always draws + low-scarcity water first within each region, independent of any solve-time + water-scarcity price. The cost is economically negligible (equivalent to a + water price ~1e-5 USD/m3-world-eq, far below the swept prices) but well above + solver tolerance, so it only resolves the tie and does not distort trade-offs. + + Note: GHG and water-scarcity pricing are applied at solve time, not build time. """ - # Water stores use Mm^3, so convert m^3 limits accordingly. - water_limits = region_water_limits * constants.MM3_PER_M3 + water_region_list = list(water_regions) + + # Global free water source feeding the tiered regional supply links. + n.carriers.add("water_supply", unit="Mm^3") + n.generators.add( + "supply:water_source", + bus="water:source", + carrier="water_source", + p_nom_extendable=True, + ) + + tiers = water_tiers[water_tiers["region"].isin(water_region_list)] + tiers = _retain_material_water_capacities(tiers, min_water_capacity_mm3) + period_str = tiers["period"].astype(int).astype(str) + tier_names = ( + "supply:water:" + + tiers["region"] + + ":p" + + period_str + + ":t" + + tiers["tier"].astype(str) + ).to_numpy() + # Each tier is surface water, renewable groundwater, or non-renewable + # groundwater. Surface and renewable groundwater both accumulate AWARE + # scarcity on impact:water_scarcity at their marginal CF (renewable + # groundwater is part of the renewable blue-water system AWARE's AMD + # counts) and carry the negligible merit-order regularizer (cost + # proportional to CF) so the unpriced LP draws low-CF water first; + # renewable groundwater additionally tallies its drawn volume 1:1 on + # impact:groundwater_renewable (bus3) as a hook for future policy. + # Non-renewable groundwater accumulates mined volume 1:1 on + # impact:groundwater_depletion instead of a CF, and carries a small real + # pumping cost that both adds realism and orders it last in the merit + # order (drawn only once renewable water in the region is exhausted). + source = tiers["source"].to_numpy() + is_nonrenewable = source == "groundwater_nonrenewable" + is_renewable_gw = source == "groundwater_renewable" + marginal_cf = tiers["marginal_cf"].to_numpy() + pumping_cost_bnusd_per_mm3 = ( + groundwater_pumping_cost_usd_per_m3 + / constants.MM3_PER_M3 + * constants.USD_TO_BNUSD + ) + n.links.add( + tier_names, + bus0="water:source", + bus1=("water:" + tiers["region"] + ":p" + period_str).to_numpy(), + bus2=np.where( + is_nonrenewable, "impact:groundwater_depletion", "impact:water_scarcity" + ), + bus3=np.where(is_renewable_gw, "impact:groundwater_renewable", ""), + carrier="water_supply", + efficiency=1.0, + efficiency2=np.where(is_nonrenewable, 1.0, marginal_cf), + efficiency3=np.where(is_renewable_gw, 1.0, 0.0), + marginal_cost=np.where( + is_nonrenewable, + pumping_cost_bnusd_per_mm3, + WATER_MERIT_ORDER_EPSILON * marginal_cf, + ), + p_nom=tiers["capacity_mm3"].to_numpy(), + p_nom_extendable=False, + region=tiers["region"].to_numpy(), + period=tiers["period"].astype(int).to_numpy(), + source=source, + ) + + # Accumulating impact stores: renewable water scarcity and non-renewable + # groundwater depletion (both priced/capped at solve time), and the + # renewable-groundwater volume tally (reporting/future policy only). + n.stores.add( + "store:impact:water_scarcity", + bus="impact:water_scarcity", + carrier="water_scarcity", + e_nom_extendable=True, + ) n.stores.add( - "store:water:" + water_limits.index, - bus="water:" + water_limits.index, - carrier="water", - e_nom=water_limits.values, - e_initial=water_limits.values, - e_nom_extendable=False, - e_cyclic=False, - region=water_limits.index, + "store:impact:groundwater_depletion", + bus="impact:groundwater_depletion", + carrier="groundwater_depletion", + e_nom_extendable=True, + ) + n.stores.add( + "store:impact:groundwater_renewable", + bus="impact:groundwater_renewable", + carrier="groundwater_renewable", + e_nom_extendable=True, ) - # Slack in water limits when using actual (current) production - if use_actual_production: + # Annual per-region groundwater (aquifer). Each region's groundwater:{region} + # bus is fed by renewable and non-renewable supply links (annual caps, same + # impact wiring as the surface tiers) and distributed to every period water + # bus by free delivery links, so the year's recharge is shared across periods + # (a dry period can draw all of it). Empty bands (non-groundwater modes) skip + # this entirely. + gw = groundwater_bands[groundwater_bands["region"].isin(water_region_list)] + gw = _retain_material_water_capacities(gw, min_water_capacity_mm3) + if not gw.empty: + n.carriers.add("groundwater", unit="Mm^3") + n.carriers.add("groundwater_delivery", unit="Mm^3") + gw_regions = sorted(gw["region"].unique()) + n.buses.add( + ["groundwater:" + r for r in gw_regions], + carrier="groundwater", + region=gw_regions, + ) + + gw_nonrenew = (gw["source"] == "groundwater_nonrenewable").to_numpy() + gw_cf = gw["marginal_cf"].to_numpy() + n.links.add( + ("supply:groundwater:" + gw["region"] + ":" + gw["source"]).to_numpy(), + bus0="water:source", + bus1=("groundwater:" + gw["region"]).to_numpy(), + bus2=np.where( + gw_nonrenew, "impact:groundwater_depletion", "impact:water_scarcity" + ), + bus3=np.where(gw_nonrenew, "", "impact:groundwater_renewable"), + carrier="water_supply", + efficiency=1.0, + efficiency2=np.where(gw_nonrenew, 1.0, gw_cf), + efficiency3=np.where(gw_nonrenew, 0.0, 1.0), + marginal_cost=np.where( + gw_nonrenew, + pumping_cost_bnusd_per_mm3, + WATER_MERIT_ORDER_EPSILON * gw_cf, + ), + p_nom=gw["capacity_mm3"].to_numpy(), + p_nom_extendable=False, + region=gw["region"].to_numpy(), + period=-1, + source=gw["source"].to_numpy(), + ) + + n_periods = int(water_periods) + deliver_region = pd.Series(np.repeat(gw_regions, n_periods)) + deliver_period = pd.Series( + np.tile(np.arange(n_periods), len(gw_regions)) + ).astype(str) + deliver_suffix = deliver_region.astype(str) + ":p" + deliver_period + n.links.add( + ("deliver:groundwater:" + deliver_suffix).to_numpy(), + bus0=("groundwater:" + deliver_region.astype(str)).to_numpy(), + bus1=("water:" + deliver_suffix).to_numpy(), + carrier="groundwater_delivery", + efficiency=1.0, + p_nom_extendable=True, + region=deliver_region.to_numpy(), + ) + + # Slack in water limits when using actual (current) production. One slack + # generator per region-period water bus, so a fixed-area baseline stays + # feasible even where a single period's supply cannot meet its demand. + if use_actual_production and water_region_list: + n_periods = int(water_periods) + slack_regions = pd.Series(np.repeat(water_region_list, n_periods)) + slack_period = pd.Series( + np.tile(np.arange(n_periods), len(water_region_list)) + ).astype(str) + slack_bus = pd.Index( + "water:" + slack_regions.astype(str) + ":p" + slack_period, dtype="object" + ) n.generators.add( - "slack:water:" + water_limits.index, - bus="water:" + water_limits.index, + "slack:" + slack_bus, + bus=slack_bus, carrier="water", marginal_cost=water_slack_cost, p_nom_extendable=True, - region=water_limits.index, + region=slack_regions.to_numpy(), ) scale_meta = n.meta.setdefault("carrier_unit_scale", {}) diff --git a/workflow/scripts/build_model/utils.py b/workflow/scripts/build_model/utils.py index a0b813a5..85336b38 100644 --- a/workflow/scripts/build_model/utils.py +++ b/workflow/scripts/build_model/utils.py @@ -126,6 +126,7 @@ def clip_negligible_coefficients( # 2. Trace irrigation water requirements and 3. near-zero carbon fluxes, # both carried as link efficiencies onto the water / CO2 buses. n_water = _clip_port_coefficients(links, bus_carrier, "water", water_floor) + n_water += _clip_port_coefficients(links, bus_carrier, "water_field", water_floor) n_co2 = _clip_port_coefficients(links, bus_carrier, "co2", co2_floor) # 4. Rounding-level cost-calibration corrections (bnUSD per Mha or Mt flow). diff --git a/workflow/scripts/build_multi_cropping.py b/workflow/scripts/build_multi_cropping.py index f4ac592b..d918130b 100644 --- a/workflow/scripts/build_multi_cropping.py +++ b/workflow/scripts/build_multi_cropping.py @@ -17,6 +17,7 @@ scale_fraction, ) from workflow.scripts.region_class_aggregation import load_cell_mapping +from workflow.scripts.water_periods import DAYS_IN_YEAR, calendar_period_shares ZONE_CAPABILITIES: dict[int, dict[str, int | bool]] = { 0: {"valid": False, "max_cycles": 0, "max_wetland_rice": 0}, @@ -66,8 +67,8 @@ def compute_eligibility_mask( overshoot the farmed cycle, so it rejects ~all observed double-cropping (including repeated same-crop combos fed identical windows). This mirrors the Stage-1 decoupling -- feasibility from observation, GAEZ only for the water - split -- and keeps the potential cap aligned with the anchored baseline - (``p_nom_max = max(potential, anchor)``). + split -- and keeps the potential cap aligned with the anchored baseline (design + section 4 correction; ``p_nom_max = max(potential, anchor)``). """ # Zone capability check (enforces cycle count and wetland-rice-cycle limit) rice_cycles = sum(1 for crop in crop_sequence if crop in WETLAND_RICE_CROPS) @@ -112,10 +113,68 @@ def compute_eligibility_mask( return combined_mask, min_fraction, total_water_arr +def compute_period_water_demand( + crop_sequence: list[str], + ws: str, + mask: np.ndarray, + start_data: dict, + length_data: dict, + water_requirement_data: dict, + water_periods: int, + region_index: np.ndarray, + calendar_tables: dict, +) -> list[np.ndarray]: + """Per-period irrigation-demand rasters (m3/ha) for a combo, on masked cells. + + Each cycle's net requirement is placed into the intra-year periods by the + observed MIRCA-OS irrigated calendar for that cycle's crop and the cell's + region (``calendar_tables[crop]`` indexed by ``region_index``). Cells whose + region has no MIRCA calendar for the crop fall back to the cycle's GAEZ + growing season; repeated same-crop cycles in that fallback are staggered by + ``365/n`` days so the second cycle lands in a different season (MIRCA already + resolves the seasons where present, so no staggering is applied there). The + per-cell sum over periods equals the summed cycle requirement (shares sum to + 1 per cycle), so the annual magnitude is preserved. + + Returns a list of ``T`` full-grid rasters; rainfed combos get all-zero rasters. + """ + periods = int(water_periods) + demand = [np.zeros(mask.shape, dtype=float) for _ in range(periods)] + if ws != "i": + return demand + idx = np.nonzero(mask) + if idx[0].size == 0: + return demand + + cell_regions = region_index[idx] # (n_masked,) region row, -1 where none + n_cycles = len(crop_sequence) + repeated = len(set(crop_sequence)) == 1 and n_cycles >= 2 + for cycle, crop in enumerate(crop_sequence): + start = start_data[(crop, ws)][idx].astype(float) + length = length_data[(crop, ws)][idx].astype(float) + if repeated: + # GAEZ-fallback stagger for identical windows (mod 365 handled by + # month_overlaps wrap); overridden per cell where MIRCA is present. + start = start + cycle * (DAYS_IN_YEAR / n_cycles) + # Per-cell observed monthly shares for this crop (zeros -> GAEZ fallback). + table = calendar_tables.get(crop) + if table is not None: + monthly = np.where(cell_regions[:, None] >= 0, table[cell_regions], 0.0) + else: + monthly = np.zeros((cell_regions.size, 12), dtype=float) + requirement = water_requirement_data[(crop, ws)][idx].astype(float) + shares, _ = calendar_period_shares(monthly, start, length, periods) + for period in range(periods): + demand[period][idx] += requirement * shares[:, period] + return demand + + if __name__ == "__main__": # Parse combinations from config combos: list[dict[str, object]] = [] use_actual_yields = bool(getattr(snakemake.params, "use_actual_yields", False)) # type: ignore[attr-defined] + water_periods = int(snakemake.params.water_periods) # type: ignore[attr-defined,name-defined] + water_cols = [f"water_requirement_m3_per_ha_p{p}" for p in range(water_periods)] combinations = effective_combinations( snakemake.config, # type: ignore[attr-defined,name-defined] @@ -140,6 +199,7 @@ def compute_eligibility_mask( } mapping_path = inputs.pop("cell_mapping") inputs.pop("combinations") + calendar_path = inputs.pop("crop_calendar") conv_csv = inputs.pop("yield_unit_conversions") moisture_csv = inputs.pop("moisture_content") @@ -148,6 +208,8 @@ def compute_eligibility_mask( suffixes = { "_yield_raster": "yield", "_suitability_raster": "suitability", + "_growing_season_start_raster": "season_start", + "_growing_season_length_raster": "season_length", "_water_requirement_raster": "water_requirement", } for key, path in inputs.items(): @@ -167,7 +229,7 @@ def compute_eligibility_mask( "resource_class", "water_supply", "eligible_area_ha", - "water_requirement_m3_per_ha", + *water_cols, ] ) empty_cycles = pd.DataFrame( @@ -218,6 +280,29 @@ def compute_eligibility_mask( ) zone_arrays[ws] = zone_arr.astype(np.int16, copy=False) + # Region row position per grid cell (-1 outside any region), derived from + # the shared cell mapping (a boundary cell resolves to one region), plus + # per-crop (n_regions, 12) MIRCA-OS monthly share tables, so the per-period + # water split places each cycle's demand in its observed months per region + # (build_mirca_crop_calendar). The calendar is area-aggregated downstream, + # so single-region cell attribution is exact enough for the timing split. + region_index = np.full(height * width, -1, dtype=np.int32) + region_index[mapping.cell_ids] = (mapping.group_ids // mapping.n_classes).astype( + np.int32 + ) + region_index = region_index.reshape(height, width) + region_pos = {region: i for i, region in enumerate(mapping.regions)} + calendar_df = pd.read_csv(calendar_path) + calendar_tables: dict[str, np.ndarray] = {} + for crop_name, grp in calendar_df.groupby("crop"): + table = np.zeros((len(mapping.regions), 12), dtype=float) + rows = grp["region"].map(region_pos) + valid = rows.notna() + table[ + rows[valid].astype(int).to_numpy(), grp.loc[valid, "month"].to_numpy() - 1 + ] = grp.loc[valid, "share"].to_numpy() + calendar_tables[str(crop_name)] = table + def conversion_factor(crop: str) -> float: base_scale = 1.0 if use_actual_yields else KG_TO_TONNE if crop in conv_df.index: @@ -227,12 +312,16 @@ def conversion_factor(crop: str) -> float: yield_data: dict[tuple[str, str], np.ndarray] = {} suitability_data: dict[tuple[str, str], np.ndarray] = {} + start_data: dict[tuple[str, str], np.ndarray] = {} + length_data: dict[tuple[str, str], np.ndarray] = {} water_requirement_data: dict[tuple[str, str], np.ndarray] = {} for (crop, ws), files in crop_files.items(): factor = conversion_factor(crop) y_arr = load_raster_array(files["yield"]) suitability_arr = load_raster_array(files["suitability"]) + start_arr = load_raster_array(files["season_start"]) + length_arr = load_raster_array(files["season_length"]) if y_arr.shape != (height, width): raise ValueError( f"Yield raster for '{crop}' ({ws}) has unexpected dimensions" @@ -241,6 +330,14 @@ def conversion_factor(crop: str) -> float: raise ValueError( f"Suitability raster for '{crop}' ({ws}) has unexpected dimensions" ) + if start_arr.shape != (height, width): + raise ValueError( + f"Growing season start raster for '{crop}' ({ws}) has unexpected dimensions" + ) + if length_arr.shape != (height, width): + raise ValueError( + f"Growing season length raster for '{crop}' ({ws}) has unexpected dimensions" + ) y_scaled = y_arr * factor if use_actual_yields and crop in moisture_lookup: @@ -248,6 +345,8 @@ def conversion_factor(crop: str) -> float: y_scaled = y_scaled * (1.0 - moisture_lookup[crop]) yield_data[(crop, ws)] = y_scaled suitability_data[(crop, ws)] = scale_fraction(suitability_arr) + start_data[(crop, ws)] = start_arr + length_data[(crop, ws)] = length_arr if ws == "i": path = files.get("water_requirement") @@ -276,7 +375,7 @@ def conversion_factor(crop: str) -> float: yield_stack = [yield_data[(crop, ws)] for crop in crop_sequence] zone_arr = zone_arrays[ws] - combined_mask, min_fraction, total_water_arr = compute_eligibility_mask( + combined_mask, min_fraction, _total_water = compute_eligibility_mask( crop_sequence, ws, zone_arr, @@ -290,6 +389,23 @@ def conversion_factor(crop: str) -> float: eligible_fraction = np.where(combined_mask, min_fraction, np.nan) eligible_area = eligible_fraction * cell_area_ha + # Per-cycle, per-period irrigation demand (m3/ha), placed by each cycle's + # observed MIRCA-OS calendar (GAEZ season, staggered for repeated + # cycles, where MIRCA is absent). Computed once per combo on the + # combined mask, then aggregated per period against the shared + # eligible-area denominator. + period_demand = compute_period_water_demand( + crop_sequence, + ws, + combined_mask, + start_data, + length_data, + water_requirement_data, + water_periods, + region_index, + calendar_tables, + ) + selected = combined_mask.ravel()[mapping.cell_ids] cells = mapping.cell_ids[selected] groups = mapping.group_ids[selected] @@ -309,19 +425,19 @@ def conversion_factor(crop: str) -> float: } ) - # Annual irrigation requirement (m3/ha): aggregate the summed-cycle - # demand*area numerator against the same eligible-area denominator. - if ws == "i" and total_water_arr is not None: + # Per-period irrigation requirement (m3/ha): aggregate each period's + # demand*area numerator against the same eligible-area denominator, so + # the sum over periods reproduces the annual requirement. + for period, col in enumerate(water_cols): + if ws != "i": + area_stats[col] = 0.0 + continue volume = np.bincount( groups, - weights=total_water_arr.ravel()[cells] * area_entries, + weights=period_demand[period].ravel()[cells] * area_entries, minlength=mapping.n_groups, ) - area_stats["water_requirement_m3_per_ha"] = ( - volume[positive] / area_by_group[positive] - ) - else: - area_stats["water_requirement_m3_per_ha"] = 0.0 + area_stats[col] = volume[positive] / area_by_group[positive] area_stats["resource_class"] = resource_classes area_stats["combination"] = combo_name @@ -334,7 +450,7 @@ def conversion_factor(crop: str) -> float: "resource_class", "water_supply", "eligible_area_ha", - "water_requirement_m3_per_ha", + *water_cols, ] ] ) @@ -372,9 +488,10 @@ def conversion_factor(crop: str) -> float: eligible_df["eligible_area_ha"] = pd.to_numeric( eligible_df["eligible_area_ha"], errors="coerce" ) - eligible_df["water_requirement_m3_per_ha"] = pd.to_numeric( - eligible_df["water_requirement_m3_per_ha"], errors="coerce" - ).fillna(0.0) + for col in water_cols: + eligible_df[col] = pd.to_numeric(eligible_df[col], errors="coerce").fillna( + 0.0 + ) eligible_df.sort_values( ["combination", "water_supply", "region", "resource_class"], inplace=True, @@ -388,7 +505,7 @@ def conversion_factor(crop: str) -> float: "resource_class", "water_supply", "eligible_area_ha", - "water_requirement_m3_per_ha", + *water_cols, ] ) diff --git a/workflow/scripts/build_region_water_availability.py b/workflow/scripts/build_region_water_availability.py deleted file mode 100644 index 1fdbafc7..00000000 --- a/workflow/scripts/build_region_water_availability.py +++ /dev/null @@ -1,353 +0,0 @@ -""" -SPDX-FileCopyrightText: 2026 Koen van Greevenbroek - -SPDX-License-Identifier: GPL-3.0-or-later -""" - -from collections.abc import Iterable -from pathlib import Path - -import geopandas as gpd -import numpy as np -import pandas as pd - -MONTH_LENGTHS = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31], dtype=float) -MONTH_ENDS = np.cumsum(MONTH_LENGTHS) -DAYS_IN_YEAR = float(MONTH_ENDS[-1]) - - -def _month_index_for_day(day: float) -> int: - """Return month index (0-based) for day in [0, 365).""" - # add tiny epsilon to avoid edge cases at month boundaries - return int(np.searchsorted(MONTH_ENDS, day + 1e-9)) - - -def compute_month_overlaps(start_day: float, length_days: float) -> np.ndarray: - """Return array of day overlaps per month for given season. - - start_day is 1-indexed (GAEZ convention). length_days can exceed 365; values - above one year are capped at 365 to avoid infinite wrap. - """ - if not np.isfinite(start_day) or not np.isfinite(length_days): - return np.zeros(12) - if length_days <= 0: - return np.zeros(12) - - start = (float(start_day) - 1.0) % DAYS_IN_YEAR - remaining = min(float(length_days), DAYS_IN_YEAR) - overlaps = np.zeros(12) - position = start - - while remaining > 1e-6: - if position >= DAYS_IN_YEAR: - position -= DAYS_IN_YEAR - month_idx = _month_index_for_day(position) - month_end = MONTH_ENDS[month_idx] - available = month_end - position - used = min(available, remaining) - overlaps[month_idx] += used - remaining -= used - position = (position + used) % DAYS_IN_YEAR - - return overlaps - - -def build_basin_region_shares( - basins_path: str, - regions: gpd.GeoDataFrame, -) -> pd.DataFrame: - basins = gpd.read_file(basins_path)[["BASIN_ID", "geometry"]] - if basins.crs != regions.crs: - regions = regions.to_crs(basins.crs) - - # Project to equal-area for accurate area calculation - area_crs = "EPSG:6933" - basins_eq = basins.to_crs(area_crs) - regions_eq = regions.to_crs(area_crs) - - basin_area = basins_eq.set_index("BASIN_ID").geometry.area / 1e6 # km² - - intersections = gpd.overlay(regions_eq, basins_eq, how="intersection") - if intersections.empty: - return pd.DataFrame(columns=["region", "basin_id", "share", "area_km2"]) - - intersections["area_km2"] = intersections.geometry.area / 1e6 - shares = intersections.groupby(["region", "BASIN_ID"], as_index=False)[ - "area_km2" - ].sum() - shares = shares.rename(columns={"BASIN_ID": "basin_id"}) - shares["share"] = shares.apply( - lambda row: row["area_km2"] / basin_area.at[row["basin_id"]], axis=1 - ) - shares = shares[shares["share"] > 1e-6] - return shares - - -def _zeros_for_regions(regions: list[str]) -> pd.DataFrame: - if not regions: - return pd.DataFrame(columns=["region", "month", "water_available_m3"]) - records = [ - {"region": region, "month": month, "water_available_m3": 0.0} - for region in regions - for month in range(1, 13) - ] - return pd.DataFrame(records) - - -def compute_region_monthly_water( - shares: pd.DataFrame, - monthly_basin: pd.DataFrame, - regions: list[str], -) -> pd.DataFrame: - required = {"basin_id", "month", "blue_water_availability_m3"} - missing = required - set(monthly_basin.columns) - if missing: - raise ValueError( - "Monthly basin data missing columns: " + ", ".join(sorted(missing)) - ) - df = shares.merge(monthly_basin, on="basin_id", how="inner") - if df.empty: - return _zeros_for_regions(regions) - df["weighted"] = df["share"] * df["blue_water_availability_m3"] - region_month = ( - df.groupby(["region", "month"], as_index=False)["weighted"] - .sum() - .rename(columns={"weighted": "water_available_m3"}) - .sort_values(["region", "month"]) - ) - region_month = region_month.set_index(["region", "month"]) - for region in regions: - if region not in region_month.index.get_level_values("region"): - filler = pd.DataFrame( - { - "region": region, - "month": range(1, 13), - "water_available_m3": 0.0, - } - ).set_index(["region", "month"]) - region_month = pd.concat([region_month, filler]) - return region_month.reset_index().sort_values(["region", "month"]) - - -def load_crop_growing_seasons( - crop_files: Iterable[str], -) -> pd.DataFrame: - records = [] - for path_str in crop_files: - path = Path(path_str) - stem = path.stem - if "_" not in stem: - continue - crop, water_supply = stem.split("_", 1) - - df = pd.read_csv(path) - - pivot = ( - df.pivot( - index=["region", "resource_class"], columns="variable", values="value" - ) - .rename_axis(columns=None) - .reset_index() - ) - - # Skip crops whose yield CSV lacks growing-season columns - # (CROPGRIDS-backed crops, which are rainfed-only and absent from - # the irrigation water-use accounting). - required = { - "suitable_area", - "growing_season_start_day", - "growing_season_length_days", - } - if not required.issubset(pivot.columns): - continue - - pivot = pivot.dropna( - subset=[ - "region", - "suitable_area", - "growing_season_start_day", - "growing_season_length_days", - ] - ) - pivot = pivot[pivot["suitable_area"] > 0] - if pivot.empty: - continue - - pivot["resource_class"] = pivot["resource_class"].astype(int) - for column in [ - "suitable_area", - "growing_season_start_day", - "growing_season_length_days", - ]: - pivot[column] = pd.to_numeric(pivot[column], errors="coerce") - - grouped = pivot.groupby("region") - for region, group in grouped: - weight = group["suitable_area"].sum() - if weight <= 0: - continue - start = ( - group["growing_season_start_day"] * group["suitable_area"] - ).sum() / weight - length = ( - group["growing_season_length_days"] * group["suitable_area"] - ).sum() / weight - records.append( - { - "region": region, - "crop": crop, - "water_supply": water_supply, - "total_area": weight, - "growing_season_start_day": start, - "growing_season_length_days": length, - } - ) - if not records: - return pd.DataFrame( - columns=[ - "region", - "crop", - "water_supply", - "total_area", - "growing_season_start_day", - "growing_season_length_days", - ] - ) - out = pd.DataFrame(records) - return out - - -def compute_region_growing_water( - region_month_water: pd.DataFrame, - crop_seasons: pd.DataFrame, - regions: list[str], -) -> pd.DataFrame: - if region_month_water.empty: - it = [] - for region in regions: - it.append( - { - "region": region, - "annual_water_available_m3": 0.0, - "growing_season_water_available_m3": 0.0, - "reference_irrigated_area": 0.0, - } - ) - return pd.DataFrame(it) - - monthly = region_month_water.set_index(["region", "month"]) # MultiIndex - - annual = ( - region_month_water.groupby("region")["water_available_m3"] - .sum() - .reindex(regions, fill_value=0.0) - .rename("annual_water_available_m3") - ) - - if crop_seasons.empty: - df = annual.to_frame().reset_index() - df["growing_season_water_available_m3"] = 0.0 - return df - - irrigated = crop_seasons[crop_seasons["water_supply"] == "i"] - if irrigated.empty: - irrigated = crop_seasons - - # Prepare container for month demand fractions per region - region_month_demand = { - region: np.zeros(12) for region in crop_seasons["region"].unique() - } - region_total_area = dict.fromkeys(crop_seasons["region"].unique(), 0.0) - - for _, row in irrigated.iterrows(): - region = row["region"] - overlaps = compute_month_overlaps( - row["growing_season_start_day"], row["growing_season_length_days"] - ) - if overlaps.sum() <= 0: - continue - area = row["total_area"] - region_total_area[region] = region_total_area.get(region, 0.0) + area - fraction = overlaps / MONTH_LENGTHS - region_month_demand[region] = ( - region_month_demand.get(region, np.zeros(12)) + area * fraction - ) - - growing_records = [] - for region, total_area in region_total_area.items(): - demand = region_month_demand.get(region) - if demand is None or total_area <= 0: - demand_fraction = np.zeros(12) - else: - demand_fraction = np.minimum(1.0, demand / max(total_area, 1e-9)) - - # Get region monthly water, fill missing months with 0 - try: - region_series = ( - monthly.loc[region]["water_available_m3"] - .reindex(range(1, 13), fill_value=0.0) - .to_numpy(dtype=float) - ) - except KeyError: - region_series = np.zeros(12) - - growing_water = float(np.dot(region_series, demand_fraction)) - growing_records.append( - { - "region": region, - "growing_season_water_available_m3": growing_water, - "reference_irrigated_area": total_area, - } - ) - - growing_df = pd.DataFrame(growing_records) - combined = ( - annual.to_frame().reset_index().merge(growing_df, on="region", how="left") - ) - combined["growing_season_water_available_m3"] = combined[ - "growing_season_water_available_m3" - ].fillna(0.0) - combined["reference_irrigated_area"] = combined["reference_irrigated_area"].fillna( - 0.0 - ) - # Ensure every region appears even if absent in both annual and growing_df - missing = [region for region in regions if region not in combined["region"].values] - if missing: - filler = pd.DataFrame( - { - "region": missing, - "annual_water_available_m3": 0.0, - "growing_season_water_available_m3": 0.0, - "reference_irrigated_area": 0.0, - } - ) - combined = pd.concat([combined, filler], ignore_index=True, sort=False) - return combined.sort_values("region").reset_index(drop=True) - - -if __name__ == "__main__": - shapefile_path: str = snakemake.input.shapefile # type: ignore[name-defined] - regions_path: str = snakemake.input.regions # type: ignore[name-defined] - monthly_csv: str = snakemake.input.monthly # type: ignore[name-defined] - crop_files = list(snakemake.input.crop_yields) # type: ignore[name-defined] - - monthly_basin_df = pd.read_csv(monthly_csv) - regions_gdf = gpd.read_file(regions_path)[["region", "geometry"]] - regions_list = regions_gdf["region"].tolist() - shares_df = build_basin_region_shares(shapefile_path, regions_gdf) - region_month_df = compute_region_monthly_water( - shares_df, monthly_basin_df, regions_list - ) - - crop_seasons_df = load_crop_growing_seasons(crop_files) - region_growing_df = compute_region_growing_water( - region_month_df, crop_seasons_df, regions_list - ) - - monthly_out = Path(snakemake.output.monthly_region) # type: ignore[name-defined] - monthly_out.parent.mkdir(parents=True, exist_ok=True) - region_month_df.to_csv(monthly_out, index=False) - - growing_out = Path(snakemake.output.region_growing) # type: ignore[name-defined] - growing_out.parent.mkdir(parents=True, exist_ok=True) - region_growing_df.to_csv(growing_out, index=False) diff --git a/workflow/scripts/build_region_water_aware.py b/workflow/scripts/build_region_water_aware.py new file mode 100644 index 00000000..d716b50f --- /dev/null +++ b/workflow/scripts/build_region_water_aware.py @@ -0,0 +1,515 @@ +""" +SPDX-FileCopyrightText: 2026 Koen van Greevenbroek + +SPDX-License-Identifier: GPL-3.0-or-later + +Build region-level irrigation water availability and a convex water-scarcity +supply curve from the AWARE2.0 dataset (Seitfudem et al. 2025, WaterGAP2.2e). + +AWARE2.0 supplies, per native basin and month: + +- ``NatAvail`` (naturalised availability), ``EWR`` (environmental reserve), + ``basin_area``, and ``AMD_final`` (availability minus demand, m3/m2/month) + in ``AWARE20_Intermediate_Variables.xlsx``; +- the irrigation-sector water demand ``2019_agri_pHWC`` in + ``AWARE20_Native_CFs.xlsx``; +- native-basin polygons in ``AWARE20_Native_CFs_geospatial.gpkg`` whose feature + id equals ``Basin_ID``. + +The published AWARE characterisation factor (CF) is marginal: +``CF = AMD_world_avg / AMD``, clipped to [0.1, 100] (and 100 where AMD <= 0). +That is only valid for small inventories. The model re-decides *all* irrigation, +so we reconstruct the non-marginal curve: as the model draws a volume ``V`` from +a basin's agricultural pool, the basin's AMD falls and the CF rises. Anchoring to +the published ``AMD_final`` (which already carries AWARE's hydrological +corrections) and adding back the agriculture the model re-decides gives, per +basin-month: + + pool = max(area * AMD_final + agri_pHWC, 0) [m3/month] + AMD0 = pool / area [m3/m2/month, no-agriculture AMD] + CF(x) = clip(AMD_world_avg / (AMD0 * (1 - x)), 0.1, 100) + +where ``x`` in [0, 1] is the fraction of the pool drawn. At ``x`` corresponding +to AWARE's 2019 irrigation draw this reproduces the published CF. GLADE then +replaces the pool capacity with WaterGAP's surface-delivery envelope while +retaining the AWARE CF curve. The convex CF curve is discretised into +sub-segments (closed-form average CF per segment) and merged across basins into +a **per region-month** merit-order supply curve, binned into ``N_TIERS`` tiers. +The lowest-CF (most abundant) water is drawn first, so a plain LP reproduces the +convex integral with no integrality. + +**Monthly resolution.** This script keeps the full monthly signal: it emits one +convex tier curve per region *and month* (12 curves per region). The temporal +resolution the model actually solves at is chosen downstream in +``compose_water_supply`` (``water.temporal_resolution``), which groups whole +months into equal periods and re-merges the monthly curves. Keeping this stage +month-resolved lets the temporal resolution change without re-running the +(expensive) AWARE basin overlay. No demand-based capping is applied here: the +seasonal bind (a month's surface use cannot exceed that month's availability) is +enforced by the LP once the period buses exist, not baked into an annual scalar. + +Outputs (all keyed by model ``region``): + +- ``monthly_region_water.csv``: agricultural pool per region-month (m3); +- ``region_growing_season_water.csv``: annual availability per region; +- ``region_water_tiers.csv``: ``region, month, tier, capacity_mm3, marginal_cf`` + -- the per-month convex water-supply curves consumed by ``compose_water_supply``; + +All volumes are WaterGAP's: its regional totals are allocated directly to the +intersecting AWARE basins before the CF tiers are built. AWARE contributes the +scarcity (CF) curve. The eta_c consumption anchor comes from +``build_region_watergap.py`` on the same basis. +""" + +import logging +from pathlib import Path + +from exactextract import exact_extract +from exactextract.raster import NumPyRasterSource +import geopandas as gpd +import numpy as np +import pandas as pd + +from workflow.scripts.build_region_watergap import ( + _WGS84_WKT, + compute_monthly_flux_raster, +) + +logger = logging.getLogger(__name__) + +MONTHS = [ + "Jan", + "Feb", + "Mar", + "Apr", + "May", + "Jun", + "Jul", + "Aug", + "Sep", + "Oct", + "Nov", + "Dec", +] + +# AWARE2.0 constants (Seitfudem et al. 2025). +AMD_WORLD_AVG = 0.02410 # m3 / m2 / month, world-average availability minus demand +CF_MIN = 0.1 +CF_MAX = 100.0 + +# Discretisation of the convex per-basin scarcity curve. Each basin-month pool is +# split into N_SUBSEGMENTS sub-segments along the cumulative-draw axis; the merged +# region-month merit-order curve is binned into N_TIERS equal-volume tiers. +N_SUBSEGMENTS = 8 +N_TIERS = 8 + +MM3_PER_M3 = 1e-6 + + +# --------------------------------------------------------------------------- +# AWARE2.0 basin loading and basin -> region crosswalk. +# --------------------------------------------------------------------------- +def load_basin_pool(intermediate_path: str, native_cfs_path: str) -> pd.DataFrame: + """Return per-basin agricultural pool and no-agriculture AMD. + + Index is ``Basin_ID``; columns are ``area`` (m2) plus, for each month, + ``pool_{Mon}`` (m3/month) and ``amd0_{Mon}`` (m3/m2/month). + """ + amd_final = pd.read_excel(intermediate_path, sheet_name="AMD_final").set_index( + "Basin_ID" + )[MONTHS] + area = pd.read_excel(intermediate_path, sheet_name="basin_area").set_index( + "Basin_ID" + )["area"] + agri = pd.read_excel(native_cfs_path, sheet_name="2019_agri_pHWC").set_index( + "Basin_ID" + )[MONTHS] + + basins = amd_final.index.intersection(area.index).intersection(agri.index) + area = area.loc[basins] + # Agricultural pool = current AMD headroom plus the irrigation demand the + # model re-decides. Clip negatives (basins already over-allocated): no + # agricultural water is available there. + pool = (amd_final.loc[basins].mul(area, axis=0) + agri.loc[basins]).clip(lower=0.0) + amd0 = pool.div(area, axis=0) + + out = pd.DataFrame(index=basins) + out["area"] = area + out[[f"pool_{m}" for m in MONTHS]] = pool.to_numpy() + out[[f"amd0_{m}" for m in MONTHS]] = amd0.to_numpy() + return out + + +def build_basin_region_cells( + basins_path: str, regions: gpd.GeoDataFrame, basin_ids: pd.Index +) -> gpd.GeoDataFrame: + """Return model-region intersections of the AWARE basins. + + The AWARE geospatial layer encodes ``Basin_ID`` as the feature id; only + basins present in ``basin_ids`` (those with pool data) are retained. The + ``share`` column is the intersection's area share of its native basin and + is used to apportion AWARE's basin-level AMD pool. Its geometry supports a + separate, direct overlay of WaterGAP surface delivery. + """ + basins = gpd.read_file( + basins_path, + layer="AWARE20_Native_CFs_geospatial", + columns=[], + fid_as_index=True, + ) + basins.index.name = "basin_id" + basins = basins[basins.index.isin(basin_ids)].reset_index() + + area_crs = "EPSG:6933" + basins_eq = basins.to_crs(area_crs) + regions_eq = regions.to_crs(area_crs) + + basin_area = basins_eq.set_index("basin_id").geometry.area + intersections = gpd.overlay(regions_eq, basins_eq, how="intersection") + if intersections.empty: + return gpd.GeoDataFrame( + columns=["region", "basin_id", "share", "geometry"], + geometry="geometry", + crs="EPSG:4326", + ) + + intersections = intersections.dissolve(["region", "basin_id"], as_index=False) + intersections["inter_area"] = intersections.geometry.area + intersections["share"] = ( + intersections["inter_area"] + / basin_area.loc[intersections["basin_id"]].to_numpy() + ) + intersections = intersections[intersections["share"] > 1e-6] + return intersections[["region", "basin_id", "share", "geometry"]].to_crs( + "EPSG:4326" + ) + + +# --------------------------------------------------------------------------- +# Convex tier construction. +# --------------------------------------------------------------------------- +def _subsegment_cf_factors(n_subsegments: int) -> np.ndarray: + """Average of ``1 / (1 - x)`` over each equal-width cumulative-draw segment. + + ``CF(x) = AMD_world_avg / (AMD0 * (1 - x))``, so the average CF of a segment + is ``(AMD_world_avg / AMD0) * factor`` with ``factor`` the closed-form mean + of ``1 / (1 - x)``: ``ln((1 - a) / (1 - b)) / (b - a)``. The final segment + (b -> 1) is capped just below 1 to keep the integral finite; the CF clip to + ``CF_MAX`` handles the divergence. + """ + edges = np.linspace(0.0, 1.0, n_subsegments + 1) + a = edges[:-1] + b = np.minimum(edges[1:], 1.0 - 1e-9) + return np.log((1.0 - a) / (1.0 - b)) / (b - a) + + +def build_region_month_tiers( + long: pd.DataFrame, n_subsegments: int, n_tiers: int +) -> pd.DataFrame: + """Merit-order convex water-supply tiers per (region, month). + + ``long`` holds one row per (region, month, basin) with columns ``region``, + ``month``, ``volume`` (m3 of agricultural pool in that month) and ``amd0`` + (m3/m2/month). Each row is expanded into ``n_subsegments`` segments of the + convex CF curve, merged per (region, month) in ascending-CF order, and + binned into ``n_tiers`` equal-volume tiers. + """ + factors = _subsegment_cf_factors(n_subsegments) + + long = long[long["volume"] > 0] + if long.empty: + return pd.DataFrame( + columns=["region", "month", "tier", "capacity_mm3", "marginal_cf"] + ) + + amd0 = long["amd0"].to_numpy()[:, None] + with np.errstate(divide="ignore", invalid="ignore"): + cf = np.where(amd0 > 0, AMD_WORLD_AVG / amd0 * factors[None, :], CF_MAX) + cf = np.clip(cf, CF_MIN, CF_MAX) + seg_volume = (long["volume"].to_numpy()[:, None] / n_subsegments) * np.ones_like(cf) + + seg = pd.DataFrame( + { + "region": np.repeat(long["region"].to_numpy(), n_subsegments), + "month": np.repeat(long["month"].to_numpy(), n_subsegments), + "cf": cf.ravel(), + "volume": seg_volume.ravel(), + } + ) + + tiers = [] + for (region, month), group in seg.groupby(["region", "month"], sort=False): + group = group.sort_values("cf") + volume = group["volume"].to_numpy() + cfs = group["cf"].to_numpy() + total = volume.sum() + if total <= 0: + continue + bin_size = total / n_tiers + cum_start = np.cumsum(volume) - volume + tier_idx = np.minimum((cum_start / bin_size).astype(int), n_tiers - 1) + for tier in range(n_tiers): + mask = tier_idx == tier + cap = volume[mask].sum() + if cap <= 0: + continue + marginal_cf = float(np.average(cfs[mask], weights=volume[mask])) + tiers.append( + { + "region": region, + "month": int(month), + "tier": tier, + "capacity_mm3": cap * MM3_PER_M3, + "marginal_cf": marginal_cf, + } + ) + + if not tiers: + return pd.DataFrame( + columns=["region", "month", "tier", "capacity_mm3", "marginal_cf"] + ) + return ( + pd.DataFrame(tiers) + .sort_values(["region", "month", "tier"]) + .reset_index(drop=True) + ) + + +def aggregate_watergap_surface_by_basin( + pirruse_path: str, + pirrusegw_path: str, + continental_area_path: str, + basin_cells: gpd.GeoDataFrame, + reference_start: int, + reference_end: int, +) -> pd.Series: + """Aggregate WaterGAP surface irrigation delivery to region-basin cells. + + Surface delivery is ``max(pirruse - pirrusegw, 0)`` on WaterGAP's native + grid. Aggregating it directly to the AWARE basin intersections preserves + WaterGAP's spatial allocation within each model region before the AWARE CF + tiers are built. + """ + irr_total, lat, lon = compute_monthly_flux_raster( + pirruse_path, + "pirruse", + continental_area_path, + reference_start, + reference_end, + ) + irr_gw, gw_lat, gw_lon = compute_monthly_flux_raster( + pirrusegw_path, + "pirrusegw", + continental_area_path, + reference_start, + reference_end, + ) + if not np.array_equal(lat, gw_lat) or not np.array_equal(lon, gw_lon): + raise ValueError("WaterGAP irrigation fields do not share a grid") + + surface = np.clip(irr_total - irr_gw, 0.0, None) + res = float(np.abs(np.diff(np.sort(np.unique(lon)))).min()) + if lat[0] < lat[-1]: + surface = np.flip(surface, axis=1) + + sources = [ + NumPyRasterSource( + surface[month], + xmin=float(lon.min()) - res / 2, + xmax=float(lon.max()) + res / 2, + ymin=float(lat.min()) - res / 2, + ymax=float(lat.max()) + res / 2, + srs_wkt=_WGS84_WKT, + name=f"m{month + 1}", + ) + for month in range(12) + ] + aggregated = exact_extract( + sources, + basin_cells, + ["sum"], + include_cols=["region", "basin_id"], + output="pandas", + ) + long = aggregated.melt( + id_vars=["region", "basin_id"], + value_vars=[f"m{month}_sum" for month in range(1, 13)], + var_name="month", + value_name="surface_m3", + ) + long["month"] = long["month"].str.extract(r"m(\d+)_sum").astype(int) + return long.set_index(["region", "basin_id", "month"])["surface_m3"] + + +def scale_pool_to_watergap_surface( + cells: pd.DataFrame, + surface_m3: pd.Series, + basin_surface_m3: pd.Series, +) -> tuple[pd.DataFrame, pd.Series]: + """Rescale AWARE pools to WaterGAP surface delivery by region-basin-month. + + AWARE ``availability`` is basin river discharge, which counts through-flow + discharge as divertible and hugely overstates the surface water accessible + to irrigation in groundwater-dependent basins (e.g. the Ogallala: an AWARE + pool ~100x the surface WaterGAP's detailed allocation actually supplies). + Its *monthly shape* is unregulated discharge timing, while WaterGAP's + ``histsoc`` runs operate every GRanD reservoir >= 0.5 km3: the monthly + profile of its irrigation surface consumption (``pirruse - pirrusegw``) is + regulated, demand-timed delivery. We keep AWARE's scarcity structure -- the + per-basin CF curve (a function of ``amd0``, not volume), but replace the + old area-weighted basin split with WaterGAP's direct grid-cell overlay. The + regional WaterGAP total remains the conservation anchor, while the direct + overlay supplies its within-region basin shares. Delivery without a mapped + AWARE basin is retained on an explicit ceiling-CF tier. + + ``surface_m3`` is indexed by ``(region, month)`` and ``basin_surface_m3`` + by ``(region, basin_id, month)``. The returned factors use the latter index. + """ + region_key = ["region", "month"] + basin_key = ["region", "basin_id", "month"] + cell_index = pd.MultiIndex.from_frame(cells[basin_key]) + region_index = pd.MultiIndex.from_frame(cells[region_key]) + pool = cells["region_pool"].to_numpy() + direct = basin_surface_m3.reindex(cell_index, fill_value=0.0).to_numpy() + + direct_total = ( + pd.Series(direct, index=region_index).groupby(level=[0, 1]).transform("sum") + ) + regional_surface = surface_m3.reindex(region_index, fill_value=0.0).to_numpy() + + shares = np.zeros_like(pool) + direct_available = direct_total.to_numpy() > 0.0 + shares[direct_available] = ( + direct[direct_available] / direct_total.to_numpy()[direct_available] + ) + target = regional_surface * shares + factor = np.divide(target, pool, out=np.zeros_like(target), where=pool > 0.0) + scaled = cells.copy() + scaled["region_pool"] = target + scaled.loc[(pool <= 0.0) & (target > 0.0), "amd0"] = 0.0 + + # AWARE's zero agricultural pool means that its AMD is non-positive after + # re-adding irrigation, so any WaterGAP delivery mapped there carries the + # method's maximum CF. If no WaterGAP basin intersection receives delivery + # despite a positive regional anchor, retain the volume on an explicit + # ceiling-CF tier instead of inventing a lower-scarcity basin allocation. + unmapped = ( + pd.Series( + regional_surface[~direct_available], index=region_index[~direct_available] + ) + .groupby(level=[0, 1]) + .first() + ) + unmapped = unmapped[unmapped > 0.0] + if not unmapped.empty: + ceiling = unmapped.rename("region_pool").reset_index() + ceiling["basin_id"] = -1 + ceiling["amd0"] = 0.0 + scaled = pd.concat( + [scaled, ceiling[["region", "basin_id", "month", "amd0", "region_pool"]]], + ignore_index=True, + ) + logger.warning( + "WaterGAP delivery lacks an AWARE basin intersection in %d " + "region-months: %.0f Mm3 assigned the AWARE ceiling CF", + len(unmapped), + float(unmapped.sum()) * MM3_PER_M3, + ) + + return scaled, pd.Series(factor, index=cell_index) + + +if __name__ == "__main__": + intermediate_path: str = snakemake.input.intermediate # type: ignore[name-defined] + native_cfs_path: str = snakemake.input.native_cfs # type: ignore[name-defined] + basins_path: str = snakemake.input.basins # type: ignore[name-defined] + regions_path: str = snakemake.input.regions # type: ignore[name-defined] + + basin_pool = load_basin_pool(intermediate_path, native_cfs_path) + + regions_gdf = gpd.read_file(regions_path)[["region", "geometry"]] + regions_list = sorted(regions_gdf["region"].tolist()) + + basin_cells = build_basin_region_cells(basins_path, regions_gdf, basin_pool.index) + shares = basin_cells[["region", "basin_id", "share"]] + + # Long table: one row per (region, basin, month) with agricultural pool and + # no-agriculture AMD. Shares apportion each basin to its overlapping regions. + pool_long = ( + basin_pool[[f"pool_{m}" for m in MONTHS]] + .rename(columns={f"pool_{m}": i + 1 for i, m in enumerate(MONTHS)}) + .rename_axis("basin_id") + .reset_index() + .melt(id_vars="basin_id", var_name="month", value_name="basin_pool") + ) + amd0_long = ( + basin_pool[[f"amd0_{m}" for m in MONTHS]] + .rename(columns={f"amd0_{m}": i + 1 for i, m in enumerate(MONTHS)}) + .rename_axis("basin_id") + .reset_index() + .melt(id_vars="basin_id", var_name="month", value_name="amd0") + ) + cells = shares.merge(pool_long, on="basin_id").merge( + amd0_long, on=["basin_id", "month"] + ) + cells["region_pool"] = cells["share"] * cells["basin_pool"] + + # Replace AWARE's basin-discharge availability volume and timing with + # WaterGAP's monthly irrigation surface consumption. WaterGAP determines + # both the regional surface envelope and its basin allocation, so the CF + # tiers retain AWARE's scarcity structure without using an area-share proxy + # for delivery within a model region. + surface_m3 = ( + pd.read_csv(snakemake.input.watergap_surface).set_index( # type: ignore[name-defined] + ["region", "month"] + )["surface_consumption_mm3"] + / MM3_PER_M3 + ) + basin_surface_m3 = aggregate_watergap_surface_by_basin( + snakemake.input.watergap_pirruse, # type: ignore[name-defined] + snakemake.input.watergap_pirrusegw, # type: ignore[name-defined] + snakemake.input.watergap_continentalarea, # type: ignore[name-defined] + basin_cells, + int(snakemake.params.surface_start), # type: ignore[name-defined] + int(snakemake.params.surface_end), # type: ignore[name-defined] + ) + cells, _ = scale_pool_to_watergap_surface(cells, surface_m3, basin_surface_m3) + + # Monthly region pool (m3) -- full agricultural availability per region-month. + monthly_region = ( + cells.groupby(["region", "month"], as_index=False)["region_pool"] + .sum() + .rename(columns={"region_pool": "water_available_m3"}) + .sort_values(["region", "month"]) + ) + + # Per (region, month) convex tiers from the full monthly pool. The seasonal + # bind is applied downstream by the LP's period buses, not here. + tier_input = cells.rename(columns={"region_pool": "volume"})[ + ["region", "month", "volume", "amd0"] + ] + tiers = build_region_month_tiers(tier_input, N_SUBSEGMENTS, N_TIERS) + + annual = ( + monthly_region.groupby("region")["water_available_m3"] + .sum() + .reindex(regions_list, fill_value=0.0) + ) + region_growing = pd.DataFrame( + { + "region": regions_list, + "annual_water_available_m3": annual.to_numpy(), + } + ) + + monthly_out = Path(snakemake.output.monthly_region) # type: ignore[name-defined] + monthly_out.parent.mkdir(parents=True, exist_ok=True) + monthly_region.to_csv(monthly_out, index=False) + + growing_out = Path(snakemake.output.region_growing) # type: ignore[name-defined] + growing_out.parent.mkdir(parents=True, exist_ok=True) + region_growing.to_csv(growing_out, index=False) + + tiers_out = Path(snakemake.output.tiers) # type: ignore[name-defined] + tiers_out.parent.mkdir(parents=True, exist_ok=True) + tiers.to_csv(tiers_out, index=False) diff --git a/workflow/scripts/build_region_watergap.py b/workflow/scripts/build_region_watergap.py new file mode 100644 index 00000000..ef914fd6 --- /dev/null +++ b/workflow/scripts/build_region_watergap.py @@ -0,0 +1,308 @@ +""" +SPDX-FileCopyrightText: 2026 Koen van Greevenbroek + +SPDX-License-Identifier: GPL-3.0-or-later + +Aggregate WaterGAP 2.2e (ISIMIP3a) fields to model regions: the irrigation +surface-water availability that caps the AWARE scarcity curve, and the renewable +and non-renewable (mined) groundwater bands. + +All fields are the standard ISIMIP3a WaterGAP2.2e output, ``obsclim`` climate / +``histsoc`` (with human water use) setup, ``gswp3-w5e5`` forcing, monthly +1901-2019, 0.5 degree. Irrigation-sector, source-split water use is published +directly, on both a withdrawal and a consumption basis: + +- ``continentalarea`` (km2): WaterGAP's static continental area, including + land and surface-water bodies but excluding ocean. It is the required volume + conversion area for WaterGAP's flux and storage fields. +- ``groundwstor`` (mm): groundwater storage compartment. Its negative long-term + trend is groundwater depletion / mining (Doll et al. 2014). +- ``pirruse`` (kg m-2 s-1 = mm/s): potential irrigation water consumption (the + evapotranspired portion), all sources. +- ``pirrusegw``: the part of ``pirruse`` supplied from groundwater. + +The model works on a consumption basis (crops draw beneficial ET, delivered from +the consumption-basis pool), so the ``use`` (consumption) variables are the right +ones. From them: + +- **irrigation surface availability** = ``pirruse - pirrusegw`` (per region and + month). This is WaterGAP's assessment of how much of irrigation's consumptive + demand its detailed water allocation supplies from surface water. It replaces + AWARE's basin-discharge availability (which counts through-flow river discharge + as divertible and so hugely overstates the accessible surface in + groundwater-dependent basins such as the Ogallala). Crucially it is kept + *monthly*: WaterGAP's ``histsoc`` runs operate every GRanD reservoir >= 0.5 km3 + (Hanasaki scheme), so the monthly timing of ``pirruse - pirrusegw`` is + regulated, demand-timed delivery -- reservoirs carry wet-season discharge into + the irrigation season inside WaterGAP. AWARE's monthly shape is unregulated + discharge timing and strands that delivery in the wet months. The AWARE + scarcity (CF) curve is kept; the per region-month volumes are rescaled to this + envelope in ``build_region_water_aware.py``. +- **mined groundwater** = the groundwater-storage decline (attributed to + irrigation downstream by the consumption pool). +- **renewable groundwater** = ``max(pirrusegw - mined, 0)``: the recharged part + of irrigation groundwater consumption. + +Outputs (keyed by model ``region``): + +- ``region_watergap_surface.csv``: ``region, month, surface_consumption_mm3`` -- + monthly climatological irrigation surface consumption, the availability + envelope for the AWARE curve; +- ``region_groundwater_depletion.csv``: ``region, mined_mm3, renewable_gw_mm3`` + -- the additive groundwater bands consumed by ``compose_water_supply.py``; +- ``region_agri_consumption.csv``: ``region, agri_consumption_m3`` -- annual + total irrigation consumption (``pirruse``), the demand anchor for ``eta_c`` + and the mining ceiling. Replaces the AWARE 2019 ``agri_pHWC`` anchor so that + every volume (supply envelope, groundwater bands, demand anchor) comes from + one WaterGAP simulation and window; AWARE then contributes the scarcity (CF) + valuation and its native basin geometry. +- ``region_watergap_demand.csv``: ``region, month, irrigation_consumption_mm3`` + -- the monthly resolution of the same ``pirruse`` climatology: WaterGAP's + demand-timed irrigation requirement (net of effective precipitation). Used to + retime crop-calendar demand shares so that region-month demand totals are + consistent with the supply envelope above (``build_mirca_crop_calendar.py``). + +Reference: + Doll et al. (2014). Global-scale assessment of groundwater depletion and + related groundwater abstractions. Water Resources Research, 50, 5698-5720. + Muller Schmied et al. (2024). WaterGAP v2.2e. Geosci. Model Dev., 17, 8817. +""" + +from pathlib import Path + +from exactextract import exact_extract +from exactextract.raster import NumPyRasterSource +import geopandas as gpd +import numpy as np +import pandas as pd +import xarray as xr + +MM_TO_M = 1e-3 +MM3_PER_M3 = 1e-6 +M2_PER_KM2 = 1e6 +WATERGAP_START_YEAR = 1901 # first year of the WaterGAP 2.2e monthly series +# Climatological month lengths (Feb averaged over leap years), summing to 365.25. +SECONDS_PER_MONTH = ( + np.array([31, 28.25, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]) * 24 * 3600.0 +) + +# WGS84 for the exactextract raster source. +_WGS84_WKT = ( + 'GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563]],' + 'PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]]' +) + + +def load_continental_area(path: str, lat: np.ndarray, lon: np.ndarray) -> np.ndarray: + """Load WaterGAP's continental cell area as m2 on the requested grid.""" + ds = xr.open_dataset(path, decode_times=False) + area = ds["continentalarea"].isel(time=0) + area_lat = ds["lat"].values.astype(float) + area_lon = ds["lon"].values.astype(float) + values = area.values.astype(float) + ds.close() + + if not np.array_equal(lat, area_lat) or not np.array_equal(lon, area_lon): + raise ValueError("WaterGAP continental area does not share the data grid") + return np.where(np.isfinite(values), values, 0.0) * M2_PER_KM2 + + +def compute_depletion_raster( + groundwstor_path: str, + continental_area_path: str, + trend_start: int, + trend_end: int, +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Return per-cell groundwater depletion (m3/yr), latitudes and longitudes. + + Depletion is the negative linear trend of annual-mean groundwater storage + over ``[trend_start, trend_end]`` (inclusive), converted from mm/yr to a + volume via cell area. Cells with a non-negative trend (stable or recovering + storage) contribute zero. + """ + ds = xr.open_dataset(groundwstor_path, decode_times=False) + storage = ds["groundwstor"] # (time, lat, lon), kg m-2 = mm + lat = ds["lat"].values.astype(float) + lon = ds["lon"].values.astype(float) + + years = np.arange(trend_start, trend_end + 1) + annual = np.empty((years.size, lat.size, lon.size), dtype=float) + for i, year in enumerate(years): + start = (year - WATERGAP_START_YEAR) * 12 + annual[i] = np.nanmean(storage.isel(time=slice(start, start + 12)).values, 0) + ds.close() + + # Ordinary-least-squares slope per cell (mm/yr). + x = years - years.mean() + slope = (x[:, None, None] * (annual - annual.mean(0))).sum(0) / (x**2).sum() + + depletion_mm_yr = np.where(slope < 0, -slope, 0.0) + continental_area_m2 = load_continental_area(continental_area_path, lat, lon) + depletion_m3 = depletion_mm_yr * MM_TO_M * continental_area_m2 + return depletion_m3, lat, lon + + +def compute_monthly_flux_raster( + path: str, + variable: str, + continental_area_path: str, + reference_start: int, + reference_end: int, +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Return a WaterGAP flux variable as per-cell monthly volumes (m3/month). + + ``variable`` is a monthly water-use flux (kg m-2 s-1 = mm/s), e.g. ``pirruse`` + or ``pirrusegw``. Averaged into a 12-month climatology over + ``[reference_start, reference_end]`` (inclusive), clipped at zero per cell-month + (negative cells are net returns / recharge) and converted to volumes via + month length and cell area. Shape (12, nlat, nlon). + """ + ds = xr.open_dataset(path, decode_times=False) + flux = ds[variable] # (time, lat, lon), kg m-2 s-1 = mm/s + lat = ds["lat"].values.astype(float) + lon = ds["lon"].values.astype(float) + + start = (reference_start - WATERGAP_START_YEAR) * 12 + end = (reference_end - WATERGAP_START_YEAR + 1) * 12 + window = flux.isel(time=slice(start, end)).values + ds.close() + clim_flux = np.nanmean(window.reshape(-1, 12, lat.size, lon.size), 0) + + volume_mm = np.clip(clim_flux, 0.0, None) * SECONDS_PER_MONTH[:, None, None] + continental_area_m2 = load_continental_area(continental_area_path, lat, lon) + volume_m3 = volume_mm * MM_TO_M * continental_area_m2[None, :, :] + return volume_m3, lat, lon + + +def aggregate_to_regions( + values_m3: np.ndarray, + lat: np.ndarray, + lon: np.ndarray, + regions_gdf: gpd.GeoDataFrame, +) -> pd.Series: + """Coverage-weighted sum of a per-cell volume (m3/yr) into regions.""" + # Repair invalid geometries before the native exactextract call: it can + # segfault on self-intersecting polygons (a handful can survive the region + # GeoJSON round-trip at fine resolution). buffer(0) keeps clean polygonal + # coverage; valid geometries are unchanged. + invalid = ~regions_gdf.geometry.is_valid + if invalid.any(): + regions_gdf = regions_gdf.copy() + regions_gdf.loc[invalid, "geometry"] = regions_gdf.loc[ + invalid, "geometry" + ].buffer(0) + + res = float(np.abs(np.diff(np.sort(np.unique(lon)))).min()) + arr = np.where(np.isfinite(values_m3), values_m3, 0.0) + # Orient north-to-south for the raster source. + if lat[0] < lat[-1]: + arr = np.flipud(arr) + ymin, ymax = float(lat.min()) - res / 2, float(lat.max()) + res / 2 + src = NumPyRasterSource( + arr, + xmin=float(lon.min()) - res / 2, + xmax=float(lon.max()) + res / 2, + ymin=ymin, + ymax=ymax, + srs_wkt=_WGS84_WKT, + ) + result = exact_extract( + src, + regions_gdf.reset_index(), + ["sum"], + include_cols=["region"], + output="pandas", + ) + return result.set_index("region")["sum"].rename("value_m3") + + +if __name__ == "__main__": + groundwstor_path: str = snakemake.input.groundwstor # type: ignore[name-defined] + continental_area_path: str = snakemake.input.continentalarea # type: ignore[name-defined] + pirruse_path: str = snakemake.input.pirruse # type: ignore[name-defined] + pirrusegw_path: str = snakemake.input.pirrusegw # type: ignore[name-defined] + regions_path: str = snakemake.input.regions # type: ignore[name-defined] + trend_start: int = int(snakemake.params.trend_start) # type: ignore[name-defined] + trend_end: int = int(snakemake.params.trend_end) # type: ignore[name-defined] + surface_start: int = int(snakemake.params.surface_start) # type: ignore[name-defined] + surface_end: int = int(snakemake.params.surface_end) # type: ignore[name-defined] + surface_out: str = snakemake.output.surface # type: ignore[name-defined] + depletion_out: str = snakemake.output.depletion # type: ignore[name-defined] + agri_out: str = snakemake.output.region_agri # type: ignore[name-defined] + demand_out: str = snakemake.output.demand # type: ignore[name-defined] + + regions_gdf = gpd.read_file(regions_path)[["region", "geometry"]] + + depletion_m3, lat, lon = compute_depletion_raster( + groundwstor_path, continental_area_path, trend_start, trend_end + ) + mined = aggregate_to_regions(depletion_m3, lat, lon, regions_gdf) + + def monthly_to_regions(path, variable): + """(region x month) DataFrame of monthly volumes (m3).""" + monthly_m3, lat, lon = compute_monthly_flux_raster( + path, variable, continental_area_path, surface_start, surface_end + ) + return pd.DataFrame( + { + m + 1: aggregate_to_regions(monthly_m3[m], lat, lon, regions_gdf) + for m in range(12) + } + ).rename_axis(columns="month") + + irr_total = monthly_to_regions(pirruse_path, "pirruse") + irr_gw = monthly_to_regions(pirrusegw_path, "pirrusegw") + + region_index = pd.Index(sorted(regions_gdf["region"]), name="region") + + # Irrigation surface availability = total irrigation consumption minus the + # groundwater-supplied part, per region-month. Clipped at zero (a region can + # be fully groundwater-supplied, e.g. the Ogallala, where this is ~0). The + # monthly timing carries WaterGAP's reservoir-regulated delivery. + surface = ( + (irr_total.sub(irr_gw, fill_value=0.0) * MM3_PER_M3) + .clip(lower=0.0) + .reindex(region_index, fill_value=0.0) + .stack() + .rename("surface_consumption_mm3") + .reset_index() + .sort_values(["region", "month"]) + ) + Path(surface_out).parent.mkdir(parents=True, exist_ok=True) + surface.to_csv(surface_out, index=False) + + # Groundwater bands (annual): mined from the storage trend; renewable = the + # recharged remainder of irrigation groundwater consumption (pirrusegw - mined). + depletion = pd.DataFrame({"region": region_index}).assign( + mined_mm3=lambda d: d["region"].map(mined * MM3_PER_M3).fillna(0.0), + renewable_gw_mm3=lambda d: ( + d["region"].map(irr_gw.sum(axis=1) * MM3_PER_M3).fillna(0.0) + - d["mined_mm3"] + ).clip(lower=0.0), + ) + Path(depletion_out).parent.mkdir(parents=True, exist_ok=True) + depletion.to_csv(depletion_out, index=False) + + # Total irrigation consumption (pirruse, annual): the demand anchor for + # eta_c and the groundwater mining ceiling, on the same basis, simulation + # and reference window as the supply envelope above. Same schema as the + # legacy AWARE anchor (region, agri_consumption_m3) so consumers are + # source-agnostic. + agri = pd.DataFrame({"region": region_index}).assign( + agri_consumption_m3=lambda d: d["region"].map(irr_total.sum(axis=1)).fillna(0.0) + ) + Path(agri_out).parent.mkdir(parents=True, exist_ok=True) + agri.to_csv(agri_out, index=False) + + # Monthly resolution of the demand anchor: WaterGAP's requirement timing + # (net of effective precipitation), consumed by the crop-calendar retiming. + demand = ( + (irr_total * MM3_PER_M3) + .reindex(region_index, fill_value=0.0) + .stack() + .rename("irrigation_consumption_mm3") + .reset_index() + .sort_values(["region", "month"]) + ) + Path(demand_out).parent.mkdir(parents=True, exist_ok=True) + demand.to_csv(demand_out, index=False) diff --git a/workflow/scripts/compose_water_supply.py b/workflow/scripts/compose_water_supply.py new file mode 100644 index 00000000..df87e031 --- /dev/null +++ b/workflow/scripts/compose_water_supply.py @@ -0,0 +1,247 @@ +""" +SPDX-FileCopyrightText: 2026 Koen van Greevenbroek + +SPDX-License-Identifier: GPL-3.0-or-later + +Compose the scenario-agnostic water-supply tables from the per-month +availability curves, grouping months into the model's temporal periods +(``water.temporal_resolution``). + +The upstream availability stage (``build_region_water_aware`` / +``process_huang_irrigation_water``) emits a convex tier curve per region *and +month*. This stage groups whole months into ``temporal_resolution`` equal +periods (month ``m`` -> period ``(m - 1) * T // 12``), re-merges the monthly +curves within each period into a single convex merit-order surface curve, and +produces two tables the model build consumes: + +- ``region_water_tiers.csv`` (``region, period, tier, capacity_mm3, marginal_cf, + source``): the **per-period surface** supply. Surface is period-bound -- a + river cannot be pumped next season without a reservoir -- so each period's cap + binds its own draw. +- ``region_groundwater_bands.csv`` (``region, source, capacity_mm3, + marginal_cf``): the **annual per-region groundwater** bands, only in + ``groundwater`` mode. Groundwater is an aquifer, an annual buffer that can be + pumped in any period, so it attaches to a single per-region groundwater bus in + the model build and is shared across periods rather than split per period. + +Keeping the month grouping here means changing the temporal resolution does not +re-run the expensive basin overlay. + +Two independent switches finish the tables: + +- ``supply.scarcity_tiers``: keep the convex per-period surface scarcity curve, + or collapse each region-period's pool to one flat tier (cf = 0) -- a simple + availability cap for studies where water is not the focus. +- ``supply.groundwater``: additionally emit the annual renewable and + non-renewable groundwater bands. The renewable groundwater (``source = + groundwater_renewable``, WaterGAP volume, priced at the scarcest surface CF) + and the non-renewable / mined band (``source = groundwater_nonrenewable``, a + generous non-binding aquifer ceiling carrying a real pumping cost that orders + it last) meet whatever surface cannot. Because groundwater is additive rather + than a relabel of surface, mining emerges endogenously wherever surface plus + renewable groundwater fall short -- it is no longer capped at renewable + availability. + +The bands are always derived from the *uncollapsed* curve, so the CF that +renewable groundwater borrows stays physical even when the surface table itself +is collapsed to a flat cap. + +The monthly and growing-season availability tables are copied through unchanged. +""" + +from pathlib import Path +import shutil + +import numpy as np +import pandas as pd + +TIER_COLUMNS = ["region", "period", "tier", "capacity_mm3", "marginal_cf", "source"] +GW_BAND_COLUMNS = ["region", "source", "capacity_mm3", "marginal_cf"] + +# Number of equal-volume tiers the merged per-period convex curve is binned into +# (matches N_TIERS in build_region_water_aware). +N_TIERS = 8 + +# AWARE characterisation-factor ceiling (matches build_region_water_aware CF_MAX); +# used to price renewable groundwater in regions that have no surface water at all +# (so no surface CF to borrow) -- treat them as maximally scarce. +CF_MAX = 100.0 + + +def month_to_period(months: np.ndarray, temporal_resolution: int) -> np.ndarray: + """Map calendar months (1..12) to period indices (0..T-1) in equal blocks. + + ``T`` must divide 12 (enforced by the config schema), so each period spans + ``12 // T`` consecutive months. + """ + return ((months.astype(int) - 1) * int(temporal_resolution)) // 12 + + +def aggregate_months_to_periods( + monthly_tiers: pd.DataFrame, temporal_resolution: int +) -> pd.DataFrame: + """Group per-month tiers into per-period convex curves. + + ``monthly_tiers`` has columns ``region, month, tier, capacity_mm3, + marginal_cf``. Returns ``region, period, tier, capacity_mm3, marginal_cf`` + with the merged convex curve re-binned to ``N_TIERS`` equal-volume tiers per + region-period. Fully vectorised: the segments are sorted into merit order, + an equal-volume tier index is assigned from the grouped cumulative volume, + and each tier's marginal CF is the volume-weighted mean of its segments. + """ + tiers = monthly_tiers.copy() + tiers["period"] = month_to_period(tiers["month"].to_numpy(), temporal_resolution) + if tiers.empty: + return pd.DataFrame( + columns=["region", "period", "tier", "capacity_mm3", "marginal_cf"] + ) + + keys = ["region", "period"] + tiers = tiers.sort_values([*keys, "marginal_cf"]).reset_index(drop=True) + volume = tiers["capacity_mm3"].to_numpy(dtype=float) + grouped_vol = tiers.groupby(keys, sort=False)["capacity_mm3"] + total = grouped_vol.transform("sum").to_numpy() + cum_start = grouped_vol.cumsum().to_numpy() - volume + with np.errstate(divide="ignore", invalid="ignore"): + raw_tier = np.where(total > 0.0, cum_start / (total / N_TIERS), 0.0) + tiers["tier"] = np.minimum(raw_tier.astype(int), N_TIERS - 1) + tiers["cf_volume"] = tiers["marginal_cf"].to_numpy() * volume + + agg = ( + tiers.groupby([*keys, "tier"], sort=True) + .agg(capacity_mm3=("capacity_mm3", "sum"), cf_volume=("cf_volume", "sum")) + .reset_index() + ) + agg = agg[agg["capacity_mm3"] > 0.0].copy() + agg["marginal_cf"] = agg["cf_volume"] / agg["capacity_mm3"] + # Renumber tiers densely 0..k-1 within each region-period (bins can be empty). + agg["tier"] = agg.groupby(keys, sort=False).cumcount() + return agg[["region", "period", "tier", "capacity_mm3", "marginal_cf"]].reset_index( + drop=True + ) + + +def collapse_single(tiers: pd.DataFrame) -> pd.DataFrame: + """One flat renewable tier per region-period (cf = 0): a simple hard cap.""" + collapsed = tiers.groupby(["region", "period"], as_index=False)[ + "capacity_mm3" + ].sum() + collapsed["tier"] = 0 + collapsed["marginal_cf"] = 0.0 + collapsed["source"] = "renewable" + return collapsed[TIER_COLUMNS] + + +def build_groundwater_bands( + surface_tiers: pd.DataFrame, + groundwater: pd.DataFrame, + agri_consumption_mm3: pd.Series, + ceiling_factor: float, +) -> pd.DataFrame: + """Annual per-region groundwater bands (renewable + non-renewable). + + Both bands are *annual*: an aquifer integrates recharge over the year and can + be pumped in any period, so -- unlike surface, which is period-bound -- they + attach to a single per-region groundwater bus in the model build, shared + across all periods. ``surface_tiers`` (the per-period surface curve) supplies + the region's scarcest surface CF and total surface volume; ``groundwater`` is + region-indexed with ``mined_mm3`` and ``renewable_gw_mm3``; + ``agri_consumption_mm3`` is region-indexed annual consumption C (Mm3). + + - ``groundwater_renewable``: the WaterGAP renewable groundwater volume, priced + at the region's scarcest surface CF so it is drawn after surface but before + mining (``CF_MAX`` where the region has no surface water to borrow a CF + from -- such regions are maximally scarce); + - ``groundwater_nonrenewable``: a generous non-binding aquifer ceiling + (``ceiling_factor * C``), cf 0, ordered last by its pumping cost. Falls back + to the region's total surface capacity where consumption C is missing/zero. + The volume actually mined is set endogenously by how far surface plus + renewable groundwater fall short of demand. + + Returned per region (columns ``GW_BAND_COLUMNS``); regions cover the full + groundwater table, including those with groundwater but no surface tiers. + """ + regions = groundwater.index + surf = surface_tiers.groupby("region").agg( + max_cf=("marginal_cf", "max"), surface_sum=("capacity_mm3", "sum") + ) + max_cf = surf["max_cf"].reindex(regions).fillna(CF_MAX) + surface_sum = surf["surface_sum"].reindex(regions).fillna(0.0) + renewable_gw = groundwater["renewable_gw_mm3"].reindex(regions).fillna(0.0) + consumption = agri_consumption_mm3.reindex(regions).fillna(0.0) + # Ceiling anchor: annual consumption, falling back to surface scale where C = 0. + anchor = consumption.where(consumption > 0.0, surface_sum) + ceiling = ceiling_factor * anchor + + base = pd.DataFrame({"region": regions}) + ren = base.assign( + source="groundwater_renewable", + capacity_mm3=renewable_gw.to_numpy(), + marginal_cf=max_cf.to_numpy(), + ) + non = base.assign( + source="groundwater_nonrenewable", + capacity_mm3=ceiling.to_numpy(), + marginal_cf=0.0, + ) + bands = pd.concat([ren, non], ignore_index=True) + bands = bands[bands["capacity_mm3"] > 0.0] + return ( + bands[GW_BAND_COLUMNS].sort_values(["region", "source"]).reset_index(drop=True) + ) + + +if __name__ == "__main__": + scarcity_tiers = bool(snakemake.params.scarcity_tiers) # type: ignore[name-defined] + groundwater_enabled = bool(snakemake.params.groundwater) # type: ignore[name-defined] + availability: str = snakemake.params.availability # type: ignore[name-defined] + temporal_resolution = int(snakemake.params.temporal_resolution) # type: ignore[name-defined] + consumed_fraction = float(snakemake.params.consumed_fraction) # type: ignore[name-defined] + monthly_tiers = pd.read_csv(snakemake.input.tiers) # type: ignore[name-defined] + + # The Huang-based current_use pool is a *withdrawal* volume; the model draws + # water on a consumption basis, so convert the pool via the consumed + # fraction (C/W) to keep the "cap at current use" semantics. + if availability == "current_use": + monthly_tiers["capacity_mm3"] *= consumed_fraction + + tiers = aggregate_months_to_periods(monthly_tiers, temporal_resolution) + + # Surface supply: per-region-period tiers (region_water_tiers.csv), either the + # convex scarcity curve or one flat cap per region-period. + if scarcity_tiers: + surface = tiers.assign(source="renewable")[TIER_COLUMNS] + else: + surface = collapse_single(tiers) + + # Groundwater supply: annual per-region bands (region_groundwater_bands.csv). + # Derived from the uncollapsed curve so the CF the renewable band borrows is + # the region's real scarcest surface CF, not the collapsed tier's zero. + if groundwater_enabled: + ceiling_factor = float(snakemake.params.groundwater_ceiling_factor) # type: ignore[name-defined] + groundwater = pd.read_csv(snakemake.input.groundwater).set_index( # type: ignore[name-defined] + "region" + ) + agri_consumption_mm3 = ( + pd.read_csv(snakemake.input.region_agri).set_index("region")[ # type: ignore[name-defined] + "agri_consumption_m3" + ] + * 1e-6 + ) + bands = build_groundwater_bands( + tiers, groundwater, agri_consumption_mm3, ceiling_factor + ) + else: + bands = pd.DataFrame(columns=GW_BAND_COLUMNS) + + out_tiers = Path(snakemake.output.tiers) # type: ignore[name-defined] + out_tiers.parent.mkdir(parents=True, exist_ok=True) + surface.sort_values(["region", "period", "tier"]).to_csv(out_tiers, index=False) + + out_bands = Path(snakemake.output.groundwater_bands) # type: ignore[name-defined] + out_bands.parent.mkdir(parents=True, exist_ok=True) + bands.to_csv(out_bands, index=False) + + # The monthly and growing-season availability tables pass through unchanged. + shutil.copy(snakemake.input.monthly, snakemake.output.monthly_region) # type: ignore[name-defined] + shutil.copy(snakemake.input.growing, snakemake.output.region_growing) # type: ignore[name-defined] diff --git a/workflow/scripts/process_huang_irrigation_water.py b/workflow/scripts/process_huang_irrigation_water.py index 57d6c042..763f1fe5 100644 --- a/workflow/scripts/process_huang_irrigation_water.py +++ b/workflow/scripts/process_huang_irrigation_water.py @@ -9,7 +9,7 @@ area before aggregating to model regions. Produces outputs compatible with the sustainable water availability data from the Water Footprint Network. -This script produces the same output format as build_region_water_availability.py +This script produces the availability tables in the shared schema so that the two data sources can be used interchangeably. Reference: @@ -243,7 +243,6 @@ def compute_region_growing_water( ) -> pd.DataFrame: """Compute growing-season weighted water availability. - This mirrors the function from build_region_water_availability.py but uses 'water_available_m3' column from monthly data. """ if region_month_water.empty: @@ -364,7 +363,7 @@ def process_huang_irrigation( Returns: Tuple of: - DataFrame with monthly region water (region, month, water_available_m3) - - DataFrame with growing season water (same format as build_region_water_availability) + - DataFrame with growing season water (shared availability schema) """ # Load the NetCDF dataset ds = xr.open_dataset(nc_path, decode_times=False) diff --git a/workflow/scripts/solve_model/core.py b/workflow/scripts/solve_model/core.py index 6b4bd68e..52b6c272 100644 --- a/workflow/scripts/solve_model/core.py +++ b/workflow/scripts/solve_model/core.py @@ -657,6 +657,98 @@ def add_ghg_pricing_to_objective(n: pypsa.Network, ghg_price_usd_per_t: float) - ) +def add_water_scarcity_pricing_to_objective( + n: pypsa.Network, + price_usd_per_m3_world_eq: float, + nonrenewable_cf: float | None, +) -> None: + """Add water-scarcity pricing to the objective function. + + Prices the accumulated water scarcity (``store:impact:water_scarcity``, in + Mm^3 world-equivalent) at solve time, mirroring GHG pricing. The tiered + water-supply links accumulate scarcity at each tier's marginal AWARE + characterisation factor, so a positive price shifts irrigation toward + low-scarcity (abundant) water. + + Non-renewable groundwater carries no AWARE CF (it is a stock outside the + renewable budget AWARE covers), so on its own the scarcity price would + make fossil mining the cheapest source everywhere. When ``nonrenewable_cf`` + is set, each mined m^3 (accumulated on + ``store:impact:groundwater_depletion``) is charged + ``nonrenewable_cf * price``, pricing a mined m^3 at the scarcity of the + exhausted renewable water it displaces. The default 100 is AWARE's + demand-exceeds-availability cutoff plus a non-renewability premium. + + Parameters + ---------- + n : pypsa.Network + The network containing the model. + price_usd_per_m3_world_eq : float + Shadow price per m^3 world-equivalent of water scarcity, in USD + (config currency year). + nonrenewable_cf : float | None + CF charged per m^3 of non-renewable groundwater; None leaves mining + unpriced here (handled separately via ``groundwater_depletion``). + """ + # Convert USD/m3-world-eq to bnUSD/Mm3-world-eq (matching model store units). + price_bnusd_per_mm3 = ( + price_usd_per_m3_world_eq / constants.MM3_PER_M3 * constants.USD_TO_BNUSD + ) + n.stores.static.at["store:impact:water_scarcity", "marginal_cost_storage"] = ( + price_bnusd_per_mm3 + ) + if nonrenewable_cf is not None: + n.stores.static.at[ + "store:impact:groundwater_depletion", "marginal_cost_storage" + ] = nonrenewable_cf * price_bnusd_per_mm3 + + +def add_water_scarcity_cap(n: pypsa.Network, cap_mm3_world_eq: float) -> None: + """Cap total water scarcity at solve time (epsilon-constraint). + + The water-scarcity store is extendable, so bounding its capacity bounds the + accumulated scarcity (Mm^3 world-equivalent). Used to trace the emissions + vs water-scarcity Pareto front by sweeping the cap while pricing GHG. + """ + n.stores.static.at["store:impact:water_scarcity", "e_nom_max"] = cap_mm3_world_eq + + +def add_groundwater_depletion_pricing_to_objective( + n: pypsa.Network, price_usd_per_m3: float +) -> None: + """Price accumulated non-renewable groundwater depletion in the objective. + + Prices the mined volume accumulated on ``store:impact:groundwater_depletion`` + (Mm^3) at solve time, mirroring the water-scarcity and GHG pricing. A + positive price shifts irrigation away from groundwater mining. Active only + in ``water.supply.mode == "groundwater"`` (otherwise the store stays empty). + + Parameters + ---------- + n : pypsa.Network + The network containing the model. + price_usd_per_m3 : float + Shadow price per m^3 of mined groundwater, in USD (config currency year). + """ + price_bnusd_per_mm3 = ( + price_usd_per_m3 / constants.MM3_PER_M3 * constants.USD_TO_BNUSD + ) + n.stores.static.at[ + "store:impact:groundwater_depletion", "marginal_cost_storage" + ] = price_bnusd_per_mm3 + + +def add_groundwater_depletion_cap(n: pypsa.Network, cap_mm3: float) -> None: + """Cap total non-renewable groundwater depletion (epsilon-constraint). + + The groundwater-depletion store is extendable, so bounding its capacity + bounds the accumulated mined volume (Mm^3). Sweeping the cap from the + baseline depletion down to zero traces how the food system reorganizes as + groundwater is ratcheted down (the core counterfactual). + """ + n.stores.static.at["store:impact:groundwater_depletion", "e_nom_max"] = cap_mm3 + + def add_food_incentives_to_objective( n: pypsa.Network, incentives_paths: list[str] ) -> None: @@ -1402,6 +1494,39 @@ def run_solve( ghg_price = float(smk.params.ghg_price) add_ghg_pricing_to_objective(n, ghg_price) + # Add water-scarcity pricing and/or cap if enabled + scarcity_priced = smk.params.water_scarcity_pricing_enabled + scarcity_capped = smk.params.water_scarcity_cap is not None + if (scarcity_priced or scarcity_capped) and not smk.params.water_scarcity_tiers: + raise ValueError( + "water_scarcity pricing/capping requires water.supply.scarcity_tiers: " + "with collapsed tiers every surface characterisation factor is zero, " + "so the scarcity store accumulates nothing and the lever is vacuous." + ) + if scarcity_priced: + nonrenewable_cf = smk.params.water_scarcity_nonrenewable_cf + if nonrenewable_cf is not None and smk.params.groundwater_pricing_enabled: + raise ValueError( + "water_scarcity.nonrenewable_cf and groundwater_depletion pricing " + "both charge the depletion store; set nonrenewable_cf to null to " + "price groundwater depletion separately." + ) + add_water_scarcity_pricing_to_objective( + n, + float(smk.params.water_scarcity_price), + None if nonrenewable_cf is None else float(nonrenewable_cf), + ) + if scarcity_capped: + add_water_scarcity_cap(n, float(smk.params.water_scarcity_cap)) + + # Add groundwater-depletion pricing and/or cap if enabled + if smk.params.groundwater_pricing_enabled: + add_groundwater_depletion_pricing_to_objective( + n, float(smk.params.groundwater_price) + ) + if smk.params.groundwater_cap is not None: + add_groundwater_depletion_cap(n, float(smk.params.groundwater_cap)) + # Update health store marginal costs to match scenario value_per_yll. # The build uses the base config value; scenarios may override it. _apply_health_pricing(n, float(smk.params.health_value_per_yll)) diff --git a/workflow/scripts/solve_namespace.py b/workflow/scripts/solve_namespace.py index 90205fe4..327f6a4d 100644 --- a/workflow/scripts/solve_namespace.py +++ b/workflow/scripts/solve_namespace.py @@ -30,6 +30,8 @@ SOLVE_TIME_CONFIG_PREFIXES = { "emissions.ghg_price", "emissions.ghg_pricing_enabled", + "water_scarcity", + "groundwater_depletion", "health.enabled", "health.value_per_yll", "health.segment_formulation", @@ -245,6 +247,7 @@ def validate_scenario_config_schemas( "luc_breakdown", "baseline_deviation", "food_prices", + "water_metrics", ) @@ -453,6 +456,14 @@ def rp(path: str) -> str: "regional_limit": eff["land"]["regional_limit"], "biofuel_demand_scale": eff["biomass"]["biofuel_demand_scale"], "ghg_pricing_enabled": eff["emissions"]["ghg_pricing_enabled"], + "water_scarcity_tiers": eff["water"]["supply"]["scarcity_tiers"], + "water_scarcity_pricing_enabled": eff["water_scarcity"]["pricing_enabled"], + "water_scarcity_price": eff["water_scarcity"]["price"], + "water_scarcity_cap": eff["water_scarcity"]["cap_mm3_world_eq"], + "water_scarcity_nonrenewable_cf": eff["water_scarcity"]["nonrenewable_cf"], + "groundwater_pricing_enabled": eff["groundwater_depletion"]["pricing_enabled"], + "groundwater_price": eff["groundwater_depletion"]["price"], + "groundwater_cap": eff["groundwater_depletion"]["cap_mm3"], "food_incentives_enabled": eff["food_incentives"]["enabled"], "equal_by_country_source": equal_source, "slack_marginal_cost": eff["validation"]["slack_marginal_cost"], diff --git a/workflow/scripts/water_periods.py b/workflow/scripts/water_periods.py new file mode 100644 index 00000000..261e90b0 --- /dev/null +++ b/workflow/scripts/water_periods.py @@ -0,0 +1,123 @@ +""" +SPDX-FileCopyrightText: 2026 Koen van Greevenbroek + +SPDX-License-Identifier: GPL-3.0-or-later + +Shared intra-year water-period helpers. + +The model resolves water in ``T`` equal intra-year periods +(``config["water"]["temporal_resolution"]``). Months (0-based) map to periods by +``month * T // 12`` (equal blocks), matching ``compose_water_supply``. These +helpers place a crop's irrigation demand into those periods by day-overlap of its +growing season, and are used by both the single-crop path +(``build_model.crops``) and the multi-cropping Stage-2 split +(``build_multi_cropping``), so the two stay consistent. +""" + +import numpy as np +import pandas as pd + +MONTH_LENGTHS = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31], dtype=float) +MONTH_ENDS = np.cumsum(MONTH_LENGTHS) +MONTH_STARTS = MONTH_ENDS - MONTH_LENGTHS +DAYS_IN_YEAR = float(MONTH_ENDS[-1]) + + +def month_overlaps(start_days: np.ndarray, length_days: np.ndarray) -> np.ndarray: + """``(N, 12)`` matrix of days each season overlaps each calendar month. + + Vectorised over the ``N`` seasons; wrap-around past new year is handled by + also intersecting each season with the ``+365``-day copy of every month + (a season is clipped to at most one year, so two copies suffice). Rows with + a missing or non-positive season are all-zero. + """ + start_days = np.asarray(start_days, dtype=float) + length_days = np.asarray(length_days, dtype=float) + start = (start_days - 1.0) % DAYS_IN_YEAR + length = np.clip(length_days, 0.0, DAYS_IN_YEAR) + end = start + length + finite = np.isfinite(start) & np.isfinite(length) & (length > 0.0) + + lo = start[:, None] + hi = end[:, None] + a = MONTH_STARTS[None, :] + b = MONTH_ENDS[None, :] + overlaps = np.clip(np.minimum(hi, b) - np.maximum(lo, a), 0.0, None) + overlaps += np.clip( + np.minimum(hi, b + DAYS_IN_YEAR) - np.maximum(lo, a + DAYS_IN_YEAR), 0.0, None + ) + overlaps[~finite] = 0.0 + return overlaps + + +def month_to_period_matrix(water_periods: int) -> np.ndarray: + """``(12, T)`` one-hot map from calendar month to intra-year period.""" + periods = int(water_periods) + matrix = np.zeros((12, periods)) + matrix[np.arange(12), (np.arange(12) * periods) // 12] = 1.0 + return matrix + + +def period_demand_shares( + start_days: np.ndarray, length_days: np.ndarray, water_periods: int +) -> np.ndarray: + """``(N, T)`` fraction of each season's irrigation demand per period. + + The requirement is apportioned by the days the growing season spends in each + month, aggregated to periods. A missing or degenerate season falls back to an + even split across periods. Fully vectorised over the ``N`` seasons. + """ + periods = int(water_periods) + overlaps = month_overlaps(start_days, length_days) # (N, 12) + shares = overlaps @ month_to_period_matrix(periods) # (N, T) + totals = shares.sum(axis=1, keepdims=True) + even = np.full_like(shares, 1.0 / periods) + return np.where(totals > 0.0, shares / np.where(totals > 0.0, totals, 1.0), even) + + +def crop_monthly_shares( + calendar: pd.DataFrame, crop: str, regions: np.ndarray +) -> np.ndarray: + """``(N, 12)`` observed monthly demand shares for ``crop`` over ``regions``. + + ``calendar`` is the long MIRCA-OS calendar table (``region, crop, month, + share``). Regions absent for this crop get an all-zero row (GAEZ fallback + downstream). The 12 columns are calendar months 1..12. + """ + regions = np.asarray(regions, dtype=object) + out = np.zeros((len(regions), 12), dtype=float) + sub = calendar[calendar["crop"] == crop] + if sub.empty: + return out + wide = ( + sub.pivot_table(index="region", columns="month", values="share", aggfunc="sum") + .reindex(index=regions, columns=range(1, 13)) + .to_numpy() + ) + return np.nan_to_num(wide, nan=0.0) + + +def calendar_period_shares( + monthly_shares: np.ndarray, + start_days: np.ndarray, + length_days: np.ndarray, + water_periods: int, +) -> np.ndarray: + """``(N, T)`` period demand shares from an observed monthly calendar. + + ``monthly_shares`` is ``(N, 12)`` of observed per-month growing-area shares + (MIRCA-OS). Rows with a positive monthly profile are binned into the ``T`` + periods; rows that are all-zero (no observed calendar for that season) fall + back to the GAEZ growing-season split ``period_demand_shares(start, length)``. + Fully vectorised. + """ + periods = int(water_periods) + monthly = np.asarray(monthly_shares, dtype=float) + binned = monthly @ month_to_period_matrix(periods) # (N, T) + totals = binned.sum(axis=1, keepdims=True) + observed = totals[:, 0] > 0.0 + fallback = period_demand_shares(start_days, length_days, periods) + shares = np.where( + totals > 0.0, binned / np.where(totals > 0.0, totals, 1.0), fallback + ) + return shares, observed From d8b1dbecd5f486311f526773b91456dfee0b3849 Mon Sep 17 00:00:00 2001 From: Koen van Greevenbroek Date: Mon, 20 Jul 2026 18:06:05 -0700 Subject: [PATCH 02/12] docs: point the API and water citations at the new water pipeline The deleted WFN availability module left a dangling autodoc entry, and moving the water narrative into docs/water.rst orphaned the Hoekstra citation in crop_production.rst while duplicating the Huang one. --- docs/api/index.rst | 22 +++++++++++++++++++++- docs/crop_production.rst | 7 ------- 2 files changed, 21 insertions(+), 8 deletions(-) diff --git a/docs/api/index.rst b/docs/api/index.rst index 5478da33..ecdd00ad 100644 --- a/docs/api/index.rst +++ b/docs/api/index.rst @@ -76,7 +76,27 @@ Water Resources :undoc-members: :show-inheritance: -.. automodule:: workflow.scripts.build_region_water_availability +.. automodule:: workflow.scripts.build_region_water_aware + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: workflow.scripts.build_region_watergap + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: workflow.scripts.compose_water_supply + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: workflow.scripts.water_periods + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: workflow.scripts.build_mirca_crop_calendar :members: :undoc-members: :show-inheritance: diff --git a/docs/crop_production.rst b/docs/crop_production.rst index 9b293ca4..cd1ceb4a 100644 --- a/docs/crop_production.rst +++ b/docs/crop_production.rst @@ -639,10 +639,3 @@ by volume):: Produced by ``rule plot_crop_trade_map`` (``workflow/scripts/plotting/plot_crop_trade_map.py``). - - -References ------------ - -.. [hoekstra2011] Hoekstra, A.Y. and Mekonnen, M.M. (2011) *Global water scarcity: monthly blue water footprint compared to blue water availability for the world's major river basins*, Value of Water Research Report Series No. 53, UNESCO-IHE, Delft, the Netherlands. http://www.waterfootprint.org/Reports/Report53-GlobalBlueWaterScarcity.pdf -.. [huang2018] Huang, Z., Hejazi, M., Li, X., Tang, Q., Vernon, C., Leng, G., Liu, Y., Doll, P., Eisner, S., Gerten, D., Hanasaki, N., and Wada, Y. (2018). Reconstruction of global gridded monthly sectoral water withdrawals for 1971-2010 and analysis of their spatiotemporal patterns. *Hydrology and Earth System Sciences*, 22, 2117-2133. https://doi.org/10.5194/hess-22-2117-2018 From 91f334b33b0c0c6b42d0179746c8e71795af0979 Mon Sep 17 00:00:00 2001 From: Koen van Greevenbroek Date: Mon, 20 Jul 2026 18:08:52 -0700 Subject: [PATCH 03/12] fix: migrate remaining configs to water.data.availability The cost-calibration and doc-figure configs still pinned the removed water.supply_scenario key, which the schema now rejects outright. --- config/calibration/cost.yaml | 3 ++- docs/config/doc_figures.yaml | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/config/calibration/cost.yaml b/config/calibration/cost.yaml index 535d69ce..e9a7f2a1 100644 --- a/config/calibration/cost.yaml +++ b/config/calibration/cost.yaml @@ -126,7 +126,8 @@ validation: slack_marginal_cost: 7.5 water: - supply_scenario: "current_use" + data: + availability: "current_use" # NB: diet.anchor_groups_to_gbd is pinned to the base config's resolved value # by tools/calibrate (via a config overlay), so disabling health here for diff --git a/docs/config/doc_figures.yaml b/docs/config/doc_figures.yaml index 98f350f4..b7c1bfc9 100644 --- a/docs/config/doc_figures.yaml +++ b/docs/config/doc_figures.yaml @@ -81,7 +81,8 @@ deviation_penalty: deviation_type: "absolute" water: - supply_scenario: "current_use" + data: + availability: "current_use" health: # Documentation figures include health-cluster maps and the diet-attributable From 9bd6ad5911ee4976d747409a9095c8c2792f729e Mon Sep 17 00:00:00 2001 From: Koen van Greevenbroek Date: Mon, 20 Jul 2026 18:26:04 -0700 Subject: [PATCH 04/12] fix: repair the rice-CH4 dial and the inline-analysis solve path Two defects a model review surfaced in the water restructure. The rice-methane sensitivity dial hardcoded bus4/efficiency4 on crop_production. Rice CH4 now sits at bus(3 + T), and more than half of it moved to crop_production_multi, which the carrier filter never covered at any resolution. On the quarterly test build the mask matched zero links, so GSA sweeps of the CH4 factor silently left rice cultivation -- about a fifth of the CH4 budget -- unscaled. The dial now sweeps every secondary port of both crop carriers, as the GHG attribution extractor already did; verified to scale rice CH4 exactly 2.00x under a 2x factor. solve_and_analyze_model never got the water params, so any config with solving.inline_analysis (both GSA configs) died with AttributeError the moment run_solve read them. Mirrored from solve_model; a static cross-check now confirms every water param run_solve reads is defined in both solve rules and in the cluster manifest. Also from the review: blank a crop water bus when its requirement is non-finite rather than attaching one with a NaN efficiency (latent, no rows hit it today); raise instead of silently doing nothing when groundwater depletion is priced or capped with the bands switched off, mirroring the scarcity guard; and state plainly in the irrigation docstring that the eta_c feasibility floor and its warning are annual, so at finer resolution a period's demand can still exceed that period's surface and is met from the annual groundwater bands. --- tools/build-docs | 2 +- workflow/rules/analysis.smk | 23 +++++++++++++++ workflow/rules/model.smk | 1 + workflow/scripts/build_model/crops.py | 10 ++++++- workflow/scripts/build_model/irrigation.py | 12 ++++++-- workflow/scripts/solve_model/core.py | 12 ++++++-- workflow/scripts/solve_model/sensitivity.py | 31 +++++++++++++++------ workflow/scripts/solve_namespace.py | 1 + 8 files changed, 76 insertions(+), 16 deletions(-) diff --git a/tools/build-docs b/tools/build-docs index a42c7d92..9e214d27 100755 --- a/tools/build-docs +++ b/tools/build-docs @@ -6,7 +6,7 @@ # Build documentation figures and Sphinx docs. # # Validation figures use a separate config (doc_validation) because several -# settings (planning_horizon, water.supply_scenario, etc.) are consumed by +# settings (planning_horizon, water.data.availability, etc.) are consumed by # processing rules that read config[...] directly — scenario overrides don't # reach them. This script runs the validation config first, then builds # everything else with the doc_figures config. diff --git a/workflow/rules/analysis.smk b/workflow/rules/analysis.smk index 08c0fac2..bb669189 100644 --- a/workflow/rules/analysis.smk +++ b/workflow/rules/analysis.smk @@ -137,6 +137,29 @@ if config["solving"]["inline_analysis"]: ghg_pricing_enabled=lambda w: get_effective_config(w.scenario)[ "emissions" ]["ghg_pricing_enabled"], + water_scarcity_tiers=config["water"]["supply"]["scarcity_tiers"], + water_groundwater=config["water"]["supply"]["groundwater"], + water_scarcity_pricing_enabled=lambda w: get_effective_config(w.scenario)[ + "water_scarcity" + ]["pricing_enabled"], + water_scarcity_price=lambda w: get_effective_config(w.scenario)[ + "water_scarcity" + ]["price"], + water_scarcity_cap=lambda w: get_effective_config(w.scenario)[ + "water_scarcity" + ]["cap_mm3_world_eq"], + water_scarcity_nonrenewable_cf=lambda w: get_effective_config(w.scenario)[ + "water_scarcity" + ]["nonrenewable_cf"], + groundwater_pricing_enabled=lambda w: get_effective_config(w.scenario)[ + "groundwater_depletion" + ]["pricing_enabled"], + groundwater_price=lambda w: get_effective_config(w.scenario)[ + "groundwater_depletion" + ]["price"], + groundwater_cap=lambda w: get_effective_config(w.scenario)[ + "groundwater_depletion" + ]["cap_mm3"], food_incentives_enabled=lambda w: get_effective_config(w.scenario)[ "food_incentives" ]["enabled"], diff --git a/workflow/rules/model.smk b/workflow/rules/model.smk index 43854553..8dabd083 100644 --- a/workflow/rules/model.smk +++ b/workflow/rules/model.smk @@ -452,6 +452,7 @@ rule solve_model: "ghg_pricing_enabled" ], water_scarcity_tiers=config["water"]["supply"]["scarcity_tiers"], + water_groundwater=config["water"]["supply"]["groundwater"], water_scarcity_pricing_enabled=lambda w: get_effective_config(w.scenario)[ "water_scarcity" ]["pricing_enabled"], diff --git a/workflow/scripts/build_model/crops.py b/workflow/scripts/build_model/crops.py index e8bb4dd0..f8c57e71 100644 --- a/workflow/scripts/build_model/crops.py +++ b/workflow/scripts/build_model/crops.py @@ -509,10 +509,18 @@ def add_regional_crop_production_links( ("water_field:" + region_str + f":p{p}").to_numpy(dtype=object) for p in range(water_periods) ] + # A non-finite requirement must blank the bus, not attach one + # with a NaN efficiency: `NaN != 0` is True. water_bus_periods = [ - np.where(water_eff_periods[:, p] != 0.0, period_bus[p], "") + np.where( + np.isfinite(water_eff_periods[:, p]) + & (water_eff_periods[:, p] != 0.0), + period_bus[p], + "", + ) for p in range(water_periods) ] + water_eff_periods = np.nan_to_num(water_eff_periods, nan=0.0) else: water_eff_periods = np.zeros((len(df), water_periods), dtype=float) water_bus_periods = [ diff --git a/workflow/scripts/build_model/irrigation.py b/workflow/scripts/build_model/irrigation.py index a4bbf263..0748c77b 100644 --- a/workflow/scripts/build_model/irrigation.py +++ b/workflow/scripts/build_model/irrigation.py @@ -60,7 +60,12 @@ def calibrate_eta_c( """Per-region consumptive irrigation efficiency ``eta_c`` in ``(0, eta_max]``. ``eta_c = clip(E_baseline / C, eta_min, eta_max)``, floored at - ``E_baseline / pool`` for baseline feasibility. Values above 1 encode + ``E_baseline / pool`` for baseline feasibility. Note the floor and the + infeasibility check below are both *annual*: at ``temporal_resolution > 1`` + surface supply is period-bound, so a region can clear the annual test while + an individual period's demand still exceeds that period's surface. Those + periods are met from the annual groundwater bands; with + ``water.supply.groundwater: false`` they fall to slack instead. Values above 1 encode deficit irrigation (observed consumption below the GAEZ full requirement); see the module docstring. Regions without baseline irrigation (E = 0) or without observed consumption (C = 0) get 1.0 (no adjustment: the delivery @@ -110,8 +115,9 @@ def calibrate_eta_c( ) if infeasible.any(): logger.warning( - "Baseline irrigation requirement E exceeds the water pool even at " - "eta_c=eta_max in %d regions (baseline-infeasible; expect slack or " + "Baseline irrigation requirement E exceeds the annual water pool " + "even at eta_c=eta_max in %d regions (baseline-infeasible; expect " + "slack or " "reallocation there): %s", int(infeasible.sum()), ", ".join(regions[infeasible][:10]), diff --git a/workflow/scripts/solve_model/core.py b/workflow/scripts/solve_model/core.py index 52b6c272..a93b7676 100644 --- a/workflow/scripts/solve_model/core.py +++ b/workflow/scripts/solve_model/core.py @@ -721,7 +721,7 @@ def add_groundwater_depletion_pricing_to_objective( Prices the mined volume accumulated on ``store:impact:groundwater_depletion`` (Mm^3) at solve time, mirroring the water-scarcity and GHG pricing. A positive price shifts irrigation away from groundwater mining. Active only - in ``water.supply.mode == "groundwater"`` (otherwise the store stays empty). + when ``water.supply.groundwater`` is true (otherwise the store stays empty). Parameters ---------- @@ -1520,7 +1520,15 @@ def run_solve( add_water_scarcity_cap(n, float(smk.params.water_scarcity_cap)) # Add groundwater-depletion pricing and/or cap if enabled - if smk.params.groundwater_pricing_enabled: + depletion_priced = smk.params.groundwater_pricing_enabled + depletion_capped = smk.params.groundwater_cap is not None + if (depletion_priced or depletion_capped) and not smk.params.water_groundwater: + raise ValueError( + "groundwater_depletion pricing/capping requires " + "water.supply.groundwater: without the groundwater bands nothing " + "is ever mined, so the lever is vacuous." + ) + if depletion_priced: add_groundwater_depletion_pricing_to_objective( n, float(smk.params.groundwater_price) ) diff --git a/workflow/scripts/solve_model/sensitivity.py b/workflow/scripts/solve_model/sensitivity.py index e7033e8f..1a23f785 100644 --- a/workflow/scripts/solve_model/sensitivity.py +++ b/workflow/scripts/solve_model/sensitivity.py @@ -207,7 +207,8 @@ def _apply_emission_factors(n: pypsa.Network, cfg: dict) -> None: """Apply multiplicative factors to emission efficiencies. Scales ALL emission sources for each gas: - - CH4: animal_production (efficiency2) + rice crop_production (efficiency4) + - CH4: animal_production (efficiency2) + rice cultivation on both crop + carriers (port swept dynamically; it shifts with water.temporal_resolution) - N2O: animal_production (efficiency4) + fertilizer_distribution (efficiency2) + residue_incorporation (efficiency, via bus1) - LUC: land_conversion + new_to_pasture (efficiency2) @@ -237,16 +238,28 @@ def _apply_emission_factors(n: pypsa.Network, cfg: dict) -> None: animal_mask.sum(), ) - # CH4 from rice cultivation (via efficiency4 on crop_production links) - rice_ch4_mask = (n.links.static["carrier"] == "crop_production") & ( - n.links.static.get("bus4", "") == "emission:ch4" - ) - if rice_ch4_mask.any(): - n.links.static.loc[rice_ch4_mask, "efficiency4"] *= ch4_factor + # CH4 from rice cultivation. The port is not fixed: it sits after the + # per-period water block on crop_production (bus(3 + T)) and after the + # cycle outputs on crop_production_multi, so sweep every secondary bus + # of both crop carriers rather than hard-coding one. + crop_carriers = ("crop_production", "crop_production_multi") + links = n.links.static + crop_mask = links["carrier"].isin(crop_carriers) + bus_cols = [c for c in links.columns if c.startswith("bus") and c[3:].isdigit()] + n_rice_ports = 0 + for bus_col in bus_cols: + eff_col = f"efficiency{bus_col[3:]}" + if eff_col not in links.columns: + continue + mask = crop_mask & (links[bus_col] == "emission:ch4") + if mask.any(): + links.loc[mask, eff_col] *= ch4_factor + n_rice_ports += int(mask.sum()) + if n_rice_ports: logger.info( - "Applied CH4 factor %.3f to %d rice crop_production links", + "Applied CH4 factor %.3f to %d rice crop-production ports", ch4_factor, - rice_ch4_mask.sum(), + n_rice_ports, ) if n2o_factor != 1.0: diff --git a/workflow/scripts/solve_namespace.py b/workflow/scripts/solve_namespace.py index 327f6a4d..119644cf 100644 --- a/workflow/scripts/solve_namespace.py +++ b/workflow/scripts/solve_namespace.py @@ -457,6 +457,7 @@ def rp(path: str) -> str: "biofuel_demand_scale": eff["biomass"]["biofuel_demand_scale"], "ghg_pricing_enabled": eff["emissions"]["ghg_pricing_enabled"], "water_scarcity_tiers": eff["water"]["supply"]["scarcity_tiers"], + "water_groundwater": eff["water"]["supply"]["groundwater"], "water_scarcity_pricing_enabled": eff["water_scarcity"]["pricing_enabled"], "water_scarcity_price": eff["water_scarcity"]["price"], "water_scarcity_cap": eff["water_scarcity"]["cap_mm3_world_eq"], From 596729091b0b29d4e8f95d192902c00378a4ba87 Mon Sep 17 00:00:00 2001 From: Koen van Greevenbroek Date: Mon, 20 Jul 2026 18:52:12 -0700 Subject: [PATCH 05/12] docs: file the GAEZ irrigation fix under Fixed, not Added --- CHANGELOG.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 954ec5f7..6723aced 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -17,9 +17,6 @@ introduce breaking changes to configuration and outputs. ### Added -- Fixed a GAEZ data artefact where a handful of cells carry a negative net - irrigation requirement, which flipped those crop links into spurious water - *producers*. Negative requirements are now clipped to zero. - Multiple cropping is now anchored to an observed baseline derived from MIRCA-OS v2 (new automated data source), using the available 2010, 2015, or 2020 release nearest `baseline_year`. A fixed, documented sequence catalog @@ -208,6 +205,9 @@ introduce breaking changes to configuration and outputs. ### Fixed +- Fixed a GAEZ data artefact where a handful of cells carry a negative net + irrigation requirement, which flipped those crop links into spurious water + *producers*. Negative requirements are now clipped to zero. - Baseline biofuel/industrial and biogas demand is enforced again. Since 2026-05-20 the crops-with-supply safety check in `add_biofuel_links` ran before any crop production links existed, so every build silently dropped From 650b6b029fea8641cd25ca8f47a41ab8970d16d0 Mon Sep 17 00:00:00 2001 From: Koen van Greevenbroek Date: Wed, 22 Jul 2026 11:33:50 -0700 Subject: [PATCH 06/12] fix: address water-restructure review findings Second review round on the rebased branch: - Implement the static cross-check promised in the previous fix commit: tests/test_solve_rule_params.py parses run_solve's smk.params reads and fails when a param is missing from solve_model, solve_and_analyze_model, or the cluster manifest (build_scenario_entry). - Warn when a water-scarcity cap runs with groundwater on and mining neither priced nor capped: nonrenewable_cf only applies under scarcity pricing, so a bare cap can be met by CF-free mining. Documented in docs/water.rst and the cap docstring. - Fail loudly when an irrigated multi-cropping link references a region without water buses, which would silently grant free irrigation at solve time (consistency checks are skipped there). - Blank zero-demand period water buses on multi-cropping links, matching the single-crop path; port offsets stay reserved. - Drop dead generality: the tier-link block in primary_resources handled groundwater sources the tier table never carries (now asserted); remove the unread water.data.groundwater config knob; remove the backward-compatibility source-column fallback in extract_water_metrics. - Report cf as NaN for non-renewable rows in the per-tier water table; their efficiency2 is a volume tally, not a characterisation factor. - Remove the orphaned Water Footprint Network availability pipeline (download/extract/process rules, scripts and doc figure); nothing references it since the AWARE restructure. - Docs: correct the nonrenewable_cf default (100, not 30), note the cross-sector renewable-groundwater subtraction, document water_metrics.parquet in the analysis chapter, fix the supply-chain diagram label, and require water_scarcity/groundwater_depletion at the schema top level. --- CHANGELOG.md | 3 +- config/default.yaml | 3 - config/schemas/config.schema.yaml | 8 +- docs/analysis.rst | 17 ++ docs/api/index.rst | 5 - docs/data_sources.rst | 22 --- docs/water.rst | 28 +-- docs/workflow.rst | 7 +- tests/test_crop_costs.py | 1 + tests/test_solve_rule_params.py | 145 +++++++++++++++ workflow/rules/documentation.smk | 23 --- workflow/rules/retrieve.smk | 19 -- workflow/rules/water.smk | 51 +----- .../scripts/analysis/extract_water_metrics.py | 12 +- workflow/scripts/build_model/crops.py | 21 ++- .../scripts/build_model/primary_resources.py | 41 ++--- .../doc_figures/water_basin_availability.py | 165 ------------------ .../process_blue_water_availability.py | 108 ------------ workflow/scripts/solve_model/core.py | 20 ++- workflow/scripts/water_periods.py | 5 +- 20 files changed, 253 insertions(+), 451 deletions(-) create mode 100644 tests/test_solve_rule_params.py delete mode 100644 workflow/scripts/doc_figures/water_basin_availability.py delete mode 100644 workflow/scripts/process_blue_water_availability.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 6723aced..a8b5a4cd 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -104,7 +104,8 @@ introduce breaking changes to configuration and outputs. consumption, withdrawal) are now distinct and separately reported. New automatic downloads: AWARE 2.0 and WaterGAP 2.2e (ISIMIP3a). The Water Footprint Network "sustainable" supply scenario and the - `water.supply_scenario` key are removed; the source is now + `water.supply_scenario` key are removed, along with the WFN availability + download/processing pipeline and its documentation figure; the source is now `water.data.availability` (`aware` or `current_use`), defaulting to `aware`. **This is a results-affecting default change** — the AWARE pool is a looser constraint than the previous binding present-day withdrawal cap. diff --git a/config/default.yaml b/config/default.yaml index 70a0c8a0..19c37e2c 100644 --- a/config/default.yaml +++ b/config/default.yaml @@ -293,9 +293,6 @@ water: # Regional water availability + scarcity pool: "aware" (AWARE2.0) or # "current_use" (Huang et al. gridded irrigation withdrawals; validation). availability: "aware" - # Groundwater band source (storage-decline depletion + renewable irrigation - # groundwater consumption); used only when supply.groundwater is true. - groundwater: "watergap" # Inclusive reference window for WaterGAP irrigation consumption, surface # delivery and the eta_c anchor. Matches AWARE2.0's recent scarcity window. surface_reference_start: 1990 diff --git a/config/schemas/config.schema.yaml b/config/schemas/config.schema.yaml index 62ec2706..6943ad87 100644 --- a/config/schemas/config.schema.yaml +++ b/config/schemas/config.schema.yaml @@ -24,6 +24,8 @@ required: - optimal_taxes - land - water + - water_scarcity + - groundwater_depletion - fertilizer - residues - emissions @@ -454,7 +456,7 @@ properties: description: "Non-renewable groundwater ceiling as a multiple of annual agricultural consumption; generous and non-binding, active only when groundwater is true" data: type: object - required: [availability, groundwater, surface_reference_start, surface_reference_end, groundwater_trend_start, groundwater_trend_end, huang_reference_year] + required: [availability, surface_reference_start, surface_reference_end, groundwater_trend_start, groundwater_trend_end, huang_reference_year] additionalProperties: false properties: availability: @@ -464,10 +466,6 @@ properties: Regional water availability + scarcity pool source: - 'aware': AWARE2.0 (Seitfudem et al. 2025, WaterGAP2.2e) convex scarcity curve and native basin geometry; pool volumes, monthly timing and within-region basin allocation set by the WaterGAP surface field - 'current_use': Huang et al. (2018) gridded irrigation water withdrawals - groundwater: - type: string - enum: [watergap] - description: "Groundwater band source (depletion + renewable net abstraction); used only when supply.groundwater is true" surface_reference_start: type: integer minimum: 1901 diff --git a/docs/analysis.rst b/docs/analysis.rst index 083629a6..ba07a76e 100644 --- a/docs/analysis.rst +++ b/docs/analysis.rst @@ -120,6 +120,23 @@ attribution is to primary (non-trade) inflows. ``feed_by_category.parquet`` and ``feed_by_animal.parquet`` are coarser views (by feed category alone, or by animal alone) that drop the source breakdown. +**water_metrics.parquet** — Irrigation water use and scarcity by region + +Reported on a consumption basis; see :doc:`water` for the underlying model and +the caveat that reported depletion is only meaningful at +``water.temporal_resolution`` > 1. + +.. csv-table:: + :header: "Column", "Type", "Unit", "Description" + + ``region``, str, –, "Model region" + ``withdrawn_mm3``, float, Mm³, "Irrigation consumption drawn from the regional pool (all sources)" + ``withdrawal_reported_mm3``, float, Mm³, "Estimated physical withdrawal (consumption / consumed fraction C/W)" + ``scarcity_mm3_eq``, float, Mm³ world-eq, "Accumulated AWARE scarcity of the CF-carrying draw" + ``groundwater_renewable_mm3``, float, Mm³, "Renewable groundwater drawn" + ``groundwater_depletion_mm3``, float, Mm³, "Non-renewable groundwater mined" + ``mean_cf``, float, –, "Draw-weighted mean AWARE CF of the CF-carrying draw (NaN where none)" + Example Usage ~~~~~~~~~~~~~ diff --git a/docs/api/index.rst b/docs/api/index.rst index ecdd00ad..001b9354 100644 --- a/docs/api/index.rst +++ b/docs/api/index.rst @@ -71,11 +71,6 @@ Yield and Land Processing Water Resources ~~~~~~~~~~~~~~~ -.. automodule:: workflow.scripts.process_blue_water_availability - :members: - :undoc-members: - :show-inheritance: - .. automodule:: workflow.scripts.build_region_water_aware :members: :undoc-members: diff --git a/docs/data_sources.rst b/docs/data_sources.rst index e280d259..c9fd2f1a 100644 --- a/docs/data_sources.rst +++ b/docs/data_sources.rst @@ -1040,28 +1040,6 @@ be updated accordingly. Water Resources Data -------------------- -Water Footprint Network -- Monthly Blue Water Availability -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Provider**: Water Footprint Network (Hoekstra & Mekonnen) - -**Description**: Monthly blue water availability for 405 GRDC river basins, provided alongside blue-water scarcity indicators. - -**Version**: Appendix VII of Hoekstra & Mekonnen (2011); ESRI shapefile + Excel workbook (monthly availability in Mm3/month) - -**Coverage**: - * Spatial: 405 GRDC river basins (global) - -**Access**: https://www.waterfootprint.org/resources/appendix/Report53_Appendix.zip - -**License**: No explicit license; citation requested. Users should evaluate whether their use qualifies as fair use and contact UNESCO-IHE for commercial applications. - -**Citation**: Hoekstra, A.Y. and Mekonnen, M.M. (2011). *Global water scarcity: monthly blue water footprint compared to blue water availability for the world's major river basins*, Value of Water Research Report Series No. 53, UNESCO-IHE, Delft, Netherlands. - -**Retrieval**: Automatic via Snakemake rules - -**Usage**: Constraining irrigated crop production by basin-level water availability. - Huang et al. -- Gridded Irrigation Water Withdrawals ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/water.rst b/docs/water.rst index 48a4cd69..f6480712 100644 --- a/docs/water.rst +++ b/docs/water.rst @@ -24,7 +24,7 @@ bus that irrigated crops draw from: .. code-block:: text - supply:water_source + water:source --(tiered supply: CF -> scarcity, groundwater-band routing)--> water:{region} <- consumption pool (C) --(irrigate:{region}, efficiency = eta_c)--> @@ -330,7 +330,10 @@ groundwater fall short of demand -- the pumping cost keeps the draw minimal, so the ceiling itself does not bind. The groundwater sizing fields come from WaterGAP 2.2e via ``build_region_watergap.py`` (see :doc:`data_sources`): mining is the groundwater-storage decline and renewable groundwater is the recharged -part of irrigation groundwater consumption (:math:`\text{pirrusegw}`). There is +part of irrigation groundwater consumption (:math:`\text{pirrusegw}`). The +subtraction crosses sectors -- the storage decline reflects all users, so heavy +municipal or industrial mining also shrinks the irrigation renewable band (a +conservative attribution). There is no endogenous inter-period surface storage; current reservoir operation enters through the WaterGAP monthly surface profile, so mining reflects the deficit under today's regulation. With ``water.supply.groundwater: false`` there are no @@ -354,7 +357,12 @@ in ``build_region_water_aware.py`` (as the model draws down a basin's pool its AMD falls and the CF rises), discretised into tiers, and drawn low-CF-first via a negligible merit-order regularizer. At solve time the accumulated scarcity can be priced (``water_scarcity.price``) or capped -(``water_scarcity.cap_mm3_world_eq``). +(``water_scarcity.cap_mm3_world_eq``). A cap alone is porous when +``water.supply.groundwater`` is on: ``nonrenewable_cf`` applies only under +scarcity *pricing*, so with mining neither priced nor capped the LP can meet +the cap by substituting CF-free mining, deterred only by the pumping cost. +Combine the cap with a ``groundwater_depletion`` price or cap for a closed +sweep (the solve logs a warning otherwise). Groundwater depletion accounting -------------------------------- @@ -373,14 +381,12 @@ scarcity pricing alone the CF-free mined band would become the cheapest source wherever the scarcity charge exceeds the pumping cost, and "relief" would be substitution into fossil groundwater rather than conservation. ``water_scarcity.nonrenewable_cf`` therefore charges each mined m3 at -``nonrenewable_cf * water_scarcity.price``. The default 30 is the -mining-volume-weighted mean local AWARE CF of the regions that actually -deplete groundwater (so a mined m3 is priced at the scarcity of the exhausted -renewable water it displaces); weights below that reintroduce the substitution -at low-to-mid prices, while 100 (AWARE's demand-exceeds-availability cutoff -plus a non-renewability premium) is the precautionary upper anchor. Set it to -``null`` to study depletion as a separate axis via the ``groundwater_depletion`` -options (enabling both pricings together is an error). +``nonrenewable_cf * water_scarcity.price``. The default 100 -- AWARE's +demand-exceeds-availability cutoff plus a non-renewability premium -- is a +precautionary anchor pricing a mined m3 at least at the scarcity of the +exhausted renewable water it displaces. Set it to ``null`` to study depletion +as a separate axis via the ``groundwater_depletion`` options (enabling both +pricings together is an error). The renewable-groundwater band additionally tallies its drawn volume on ``impact:groundwater_renewable`` (via a ``bus3`` output) purely for reporting diff --git a/docs/workflow.rst b/docs/workflow.rst index 7bb4cac3..e26ca716 100644 --- a/docs/workflow.rst +++ b/docs/workflow.rst @@ -109,12 +109,6 @@ Data Preparation Rules * **Script**: ``workflow/scripts/build_grassland_yields.py`` * **Purpose**: Aggregate grassland yields for grazing production -**process_blue_water_availability** - * **Input**: Water Footprint Network shapefile + Excel workbook - * **Output**: ``processing/{name}/water/blue_water_availability.csv`` - * **Script**: ``workflow/scripts/process_blue_water_availability.py`` - * **Purpose**: Build monthly basin-level blue water availability - **build_region_watergap** * **Input**: WaterGAP 2.2e (ISIMIP3a) groundwater storage, potential irrigation consumption total and from groundwater, continental area, regions * **Output**: ``processing/{name}/water/watergap/`` -- ``region_watergap_surface.csv`` (monthly irrigation surface consumption), ``region_groundwater_depletion.csv``, ``region_agri_consumption.csv`` (the eta_c and mining-ceiling anchor), ``region_watergap_demand.csv`` (monthly demand, the calendar retiming target) @@ -130,6 +124,7 @@ Data Preparation Rules **build_region_water_current_use** * **Input**: Huang et al. gridded irrigation withdrawals, regions, crop yields * **Output**: ``processing/{name}/water/current_use/`` (same schema) + * **Script**: ``workflow/scripts/process_huang_irrigation_water.py`` * **Purpose**: Present-day withdrawal alternative, selected by ``water.data.availability`` **build_mirca_crop_calendar** diff --git a/tests/test_crop_costs.py b/tests/test_crop_costs.py index 388edc68..73aa2b1b 100644 --- a/tests/test_crop_costs.py +++ b/tests/test_crop_costs.py @@ -252,6 +252,7 @@ def test_multi_cropping_rice_emits_methane_per_cycle(): "crop:wetland-rice:USA", "fertilizer:USA", "emission:ch4", + "water_field:regionA:p0", ] ) eligible_area = pd.DataFrame( diff --git a/tests/test_solve_rule_params.py b/tests/test_solve_rule_params.py new file mode 100644 index 00000000..d4a740d9 --- /dev/null +++ b/tests/test_solve_rule_params.py @@ -0,0 +1,145 @@ +# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek +# +# SPDX-License-Identifier: GPL-3.0-or-later + +"""Static cross-check of run_solve's params against its three harnesses. + +``run_solve`` executes through the ``solve_model`` rule, the +``solve_and_analyze_model`` rule (inline analysis), and the cluster manifest +(``tools/cluster-solve`` via ``build_scenario_entry``). A param added to one +harness but not the others fails only at runtime, in whichever path was +forgotten. This test parses all four sources statically and fails on any +param ``run_solve`` reads that a harness does not define. +""" + +import ast +from pathlib import Path +import re + +ROOT = Path(__file__).resolve().parents[1] + + +def _run_solve_param_reads() -> set[str]: + """Attribute names read as ``smk.params.`` inside run_solve.""" + tree = ast.parse((ROOT / "workflow/scripts/solve_model/core.py").read_text()) + fn = next( + node + for node in ast.walk(tree) + if isinstance(node, ast.FunctionDef) and node.name == "run_solve" + ) + return { + node.attr + for node in ast.walk(fn) + if isinstance(node, ast.Attribute) + and isinstance(node.value, ast.Attribute) + and node.value.attr == "params" + and isinstance(node.value.value, ast.Name) + and node.value.value.id == "smk" + } + + +def _rule_param_keys(rel_path: str, rule_name: str) -> set[str]: + """Param names of a Snakemake rule, by indentation-aware text scan. + + snakefmt guarantees the layout this relies on: section headers one level + below the rule keyword, param entries exactly one level below ``params:`` + (lambda continuation lines sit deeper and carry no ``name=`` prefix). + """ + lines = (ROOT / rel_path).read_text().splitlines() + for index, line in enumerate(lines): + if line.strip() == f"rule {rule_name}:": + rule_indent = len(line) - len(line.lstrip()) + start = index + break + else: + raise AssertionError(f"rule {rule_name} not found in {rel_path}") + + keys: set[str] = set() + params_indent: int | None = None + for line in lines[start + 1 :]: + if not line.strip(): + continue + indent = len(line) - len(line.lstrip()) + if indent <= rule_indent: + break + if line.strip() == "params:": + params_indent = indent + continue + if params_indent is None: + continue + if indent <= params_indent: + params_indent = None # left the params section + continue + if indent == params_indent + 4: + match = re.match(r"(\w+)=", line.strip()) + if match: + keys.add(match.group(1)) + if not keys: + raise AssertionError(f"no params parsed for rule {rule_name} in {rel_path}") + return keys + + +def _manifest_param_keys() -> set[str]: + """Keys of the params dict built by solve_namespace.build_scenario_entry.""" + tree = ast.parse((ROOT / "workflow/scripts/solve_namespace.py").read_text()) + fn = next( + node + for node in ast.walk(tree) + if isinstance(node, ast.FunctionDef) and node.name == "build_scenario_entry" + ) + keys: set[str] = set() + for node in ast.walk(fn): + if not isinstance(node, ast.Assign) or len(node.targets) != 1: + continue + target = node.targets[0] + # params: dict = {...} appears as AnnAssign; plain params = {...} as Assign + if ( + isinstance(target, ast.Name) + and target.id == "params" + and isinstance(node.value, ast.Dict) + ): + keys.update( + key.value for key in node.value.keys if isinstance(key, ast.Constant) + ) + # Conditional additions: params["key"] = ... + if ( + isinstance(target, ast.Subscript) + and isinstance(target.value, ast.Name) + and target.value.id == "params" + and isinstance(target.slice, ast.Constant) + ): + keys.add(target.slice.value) + for node in ast.walk(fn): + if ( + isinstance(node, ast.AnnAssign) + and isinstance(node.target, ast.Name) + and node.target.id == "params" + and isinstance(node.value, ast.Dict) + ): + keys.update( + key.value for key in node.value.keys if isinstance(key, ast.Constant) + ) + if not keys: + raise AssertionError("no params keys parsed from build_scenario_entry") + return keys + + +def test_run_solve_params_defined_in_all_harnesses(): + reads = _run_solve_param_reads() + assert reads, "no smk.params reads found in run_solve" + harnesses = { + "solve_model rule (workflow/rules/model.smk)": _rule_param_keys( + "workflow/rules/model.smk", "solve_model" + ), + "solve_and_analyze_model rule (workflow/rules/analysis.smk)": _rule_param_keys( + "workflow/rules/analysis.smk", "solve_and_analyze_model" + ), + "cluster manifest (solve_namespace.build_scenario_entry)": ( + _manifest_param_keys() + ), + } + for harness, defined in harnesses.items(): + missing = reads - defined + assert ( + not missing + ), f"params read by run_solve but not defined in {harness}: {sorted(missing)}" diff --git a/workflow/rules/documentation.smk b/workflow/rules/documentation.smk index 0835eb81..e179c4f3 100644 --- a/workflow/rules/documentation.smk +++ b/workflow/rules/documentation.smk @@ -44,7 +44,6 @@ DOC_FIGURES = [ "multi_cropping_potential_rainfed", "multi_cropping_potential_irrigated", # Water availability figures - "water_basin_availability", "water_region_availability", "irrigated_land_fraction", # Livestock figures @@ -306,28 +305,6 @@ rule doc_fig_multi_cropping_potential_irrigated: "../scripts/doc_figures/multi_cropping_potential.py" -rule doc_fig_water_basin_availability: - """Generate basin water availability map.""" - input: - basin_shapefile="data/downloads/Report53_Appendix/Report53-BlueWaterScarcity-ArcGIS-ShapeFile/Monthly_WS_GRDC_405_basins.shp", - water_data=f"/{DOC_FIG_NAME}/water/blue_water_availability.csv", - style=DOC_FIG_STYLE, - output: - svg="docs/_static/figures/water_basin_availability.svg", - png="docs/_static/figures/water_basin_availability.png", - group: - "analysis_plot" - resources: - runtime="10m", - mem_mb=2000, - log: - "/shared/doc_fig_water_basin_availability.log", - benchmark: - "/shared/doc_fig_water_basin_availability.tsv" - script: - "../scripts/doc_figures/water_basin_availability.py" - - rule doc_fig_water_region_availability: """Generate regional water availability map.""" input: diff --git a/workflow/rules/retrieve.smk b/workflow/rules/retrieve.smk index 88891911..880c8b52 100644 --- a/workflow/rules/retrieve.smk +++ b/workflow/rules/retrieve.smk @@ -1140,25 +1140,6 @@ rule download_wpp_population: """ -rule download_waterfootprint_appendix: - output: - "data/downloads/Report53_Appendix.zip", - params: - url="https://www.waterfootprint.org/resources/appendix/Report53_Appendix.zip", - resources: - runtime="30m", - mem_mb=500, - log: - "/shared/download_waterfootprint_appendix.log", - benchmark: - "/shared/download_waterfootprint_appendix.tsv" - shell: - r""" - mkdir -p "$(dirname {output})" - curl -L --fail --progress-bar -o "{output}" "{params.url}" > {log} 2>&1 - """ - - rule download_huang_irrigation_water: output: temp("data/downloads/huang_irrigation_water_v2.7z"), diff --git a/workflow/rules/water.smk b/workflow/rules/water.smk index 80f19605..265d494a 100644 --- a/workflow/rules/water.smk +++ b/workflow/rules/water.smk @@ -5,16 +5,13 @@ """ Water and fertilizer-related data preparation rules. -Includes fertilizer application rates, blue water availability, -and regional water resource calculations. - -Water supply scenario: +Water availability source (``water.data.availability``): - "aware": Uses AWARE2.0 (Seitfudem et al. 2025, WaterGAP2.2e) naturalised availability and a convex water-scarcity supply curve. - "current_use": Uses Huang et al. (2018) gridded irrigation water withdrawals (validation/benchmarking; a single zero-scarcity supply tier). -Both scenarios emit a ``region_water_tiers.csv`` describing the regional water +Both sources emit a ``region_water_tiers.csv`` describing the regional water supply as one or more tiers (capacity in Mm3, marginal scarcity characterisation factor); ``aware`` resolves it to a convex merit-order curve while ``current_use`` emits a single zero-CF tier reproducing a hard availability cap. @@ -72,46 +69,6 @@ rule derive_global_fertilizer_rates: "../scripts/derive_global_fertilizer_rates.py" -rule extract_waterfootprint_appendix: - input: - zip_path="data/downloads/Report53_Appendix.zip", - output: - shapefile="data/downloads/Report53_Appendix/Report53-BlueWaterScarcity-ArcGIS-ShapeFile/Monthly_WS_GRDC_405_basins.shp", - excel="data/downloads/Report53_Appendix/Report53-Appendices-VI-IX.xls", - group: - "prep" - resources: - runtime="1m", - mem_mb=200, - log: - "/shared/extract_waterfootprint_appendix.log", - benchmark: - "/shared/extract_waterfootprint_appendix.tsv" - shell: - r""" - unzip -o {input.zip_path} -d data/downloads > {log} 2>&1 - """ - - -rule process_blue_water_availability: - input: - shapefile=rules.extract_waterfootprint_appendix.output.shapefile, - excel=rules.extract_waterfootprint_appendix.output.excel, - output: - "/{name}/water/blue_water_availability.csv", - group: - "prep" - resources: - runtime="1m", - mem_mb=200, - log: - "/{name}/process_blue_water_availability.log", - benchmark: - "/{name}/process_blue_water_availability.tsv" - script: - "../scripts/process_blue_water_availability.py" - - def crop_yield_file_list(w): return list(yield_inputs(w).values()) @@ -297,8 +254,8 @@ def water_groundwater_input(w): # Compose the scenario-agnostic water-supply tables from the selected -# availability source, tagging each tier with a `source` (renewable | -# groundwater). +# availability source: per-period surface tiers (source "renewable") and, +# with supply.groundwater, the annual per-region groundwater bands. rule compose_water_supply: input: unpack(water_availability_inputs), diff --git a/workflow/scripts/analysis/extract_water_metrics.py b/workflow/scripts/analysis/extract_water_metrics.py index c91130df..061f04f4 100644 --- a/workflow/scripts/analysis/extract_water_metrics.py +++ b/workflow/scripts/analysis/extract_water_metrics.py @@ -42,6 +42,7 @@ modulo solver tolerance. """ +import numpy as np import pandas as pd import pypsa @@ -50,7 +51,11 @@ def _water_supply_draw(n: pypsa.Network) -> pd.DataFrame: - """Return a per-tier-link frame with region, CF, drawn volume (Mm3), source.""" + """Return a per-tier-link frame with region, CF, drawn volume (Mm3), source. + + ``cf`` is NaN for non-renewable rows: their ``efficiency2`` is the 1:1 + mined-volume tally, not an AWARE characterisation factor. + """ links = n.links.static tiers = links[links["carrier"] == "water_supply"] if tiers.empty: @@ -58,12 +63,11 @@ def _water_supply_draw(n: pypsa.Network) -> pd.DataFrame: # pipeline stays config-agnostic; callers that require it check emptiness. return pd.DataFrame(columns=["region", "cf", "draw_mm3", "source"]) draw = n.links.dynamic.p0.iloc[0].reindex(tiers.index).clip(lower=0.0) - # Pre-source solved networks had only renewable tiers. - source = tiers["source"].to_numpy() if "source" in tiers.columns else "renewable" + source = tiers["source"].to_numpy() return pd.DataFrame( { "region": tiers["region"].to_numpy(), - "cf": tiers["efficiency2"].to_numpy(), + "cf": np.where(source == NONRENEWABLE, np.nan, tiers["efficiency2"]), "draw_mm3": draw.to_numpy(), "source": source, }, diff --git a/workflow/scripts/build_model/crops.py b/workflow/scripts/build_model/crops.py index f8c57e71..d4b84dd7 100644 --- a/workflow/scripts/build_model/crops.py +++ b/workflow/scripts/build_model/crops.py @@ -1375,6 +1375,18 @@ def _water_gate(df): return water_valid, _ = _water_gate(index_df) + # A water_field bus missing for an irrigated link's region would silently + # drop its water ports at solve time (consistency checks are skipped there), + # granting free irrigation. Fail loudly instead. + field_bus = "water_field:" + index_df["region"].astype(str) + ":p0" + dangling = water_valid & ~field_bus.isin(n.buses.static.index).to_numpy() + if dangling.any(): + missing = sorted(index_df.loc[dangling, "region"].unique()) + raise ValueError( + "Irrigated multi-cropping links reference regions without water " + f"buses: {missing[:10]}" + ) + # bus0 is land in Mha, water buses are Mm3, so each period coefficient is # m3/ha (numerically equal to Mm3/Mha). The per-period columns already carry # each cycle's calendar-placed requirement, so each period's own requirement @@ -1458,7 +1470,8 @@ def _water_gate(df): if not water_src.empty: # One water entry per period: buses at offsets crop_count+1 .. crop_count+T # (right after the crop outputs), each carrying its own calendar-placed - # requirement. A zero-demand period keeps its reserved port. + # requirement. A zero-demand period keeps its reserved offset but blanks + # the bus, matching the single-crop path. period_frames = [] for p in range(water_periods): w = water_src.copy() @@ -1467,8 +1480,12 @@ def _water_gate(df): w["bus_col"] = "bus" + offset_str w["eff_col"] = "efficiency" + offset_str w.loc[w["offset"].eq(1), "eff_col"] = "efficiency" - w["bus_value"] = "water_field:" + w["region"].astype(str) + f":p{p}" w["eff_value"] = w[f"water_eff_p{p}"] + w["bus_value"] = np.where( + w["eff_value"].to_numpy() != 0.0, + "water_field:" + w["region"].astype(str) + f":p{p}", + "", + ) period_frames.append( w[["link_name", "bus_col", "bus_value", "eff_col", "eff_value"]] ) diff --git a/workflow/scripts/build_model/primary_resources.py b/workflow/scripts/build_model/primary_resources.py index ce528e5f..e87d3923 100644 --- a/workflow/scripts/build_model/primary_resources.py +++ b/workflow/scripts/build_model/primary_resources.py @@ -117,6 +117,11 @@ def add_primary_resources( tiers = water_tiers[water_tiers["region"].isin(water_region_list)] tiers = _retain_material_water_capacities(tiers, min_water_capacity_mm3) + if not (tiers["source"] == "renewable").all(): + raise ValueError( + "region_water_tiers.csv must contain only surface tiers " + "(source='renewable'); groundwater belongs in the bands table" + ) period_str = tiers["period"].astype(int).astype(str) tier_names = ( "supply:water:" @@ -126,21 +131,11 @@ def add_primary_resources( + ":t" + tiers["tier"].astype(str) ).to_numpy() - # Each tier is surface water, renewable groundwater, or non-renewable - # groundwater. Surface and renewable groundwater both accumulate AWARE - # scarcity on impact:water_scarcity at their marginal CF (renewable - # groundwater is part of the renewable blue-water system AWARE's AMD - # counts) and carry the negligible merit-order regularizer (cost - # proportional to CF) so the unpriced LP draws low-CF water first; - # renewable groundwater additionally tallies its drawn volume 1:1 on - # impact:groundwater_renewable (bus3) as a hook for future policy. - # Non-renewable groundwater accumulates mined volume 1:1 on - # impact:groundwater_depletion instead of a CF, and carries a small real - # pumping cost that both adds realism and orders it last in the merit - # order (drawn only once renewable water in the region is exhausted). - source = tiers["source"].to_numpy() - is_nonrenewable = source == "groundwater_nonrenewable" - is_renewable_gw = source == "groundwater_renewable" + # Surface tiers (source is always "renewable") accumulate AWARE scarcity + # on impact:water_scarcity at their marginal CF and carry the negligible + # merit-order regularizer (cost proportional to CF) so the unpriced LP + # draws low-CF water first. Groundwater never appears here; it is supplied + # through the annual per-region bands below. marginal_cf = tiers["marginal_cf"].to_numpy() pumping_cost_bnusd_per_mm3 = ( groundwater_pumping_cost_usd_per_m3 @@ -151,24 +146,16 @@ def add_primary_resources( tier_names, bus0="water:source", bus1=("water:" + tiers["region"] + ":p" + period_str).to_numpy(), - bus2=np.where( - is_nonrenewable, "impact:groundwater_depletion", "impact:water_scarcity" - ), - bus3=np.where(is_renewable_gw, "impact:groundwater_renewable", ""), + bus2="impact:water_scarcity", carrier="water_supply", efficiency=1.0, - efficiency2=np.where(is_nonrenewable, 1.0, marginal_cf), - efficiency3=np.where(is_renewable_gw, 1.0, 0.0), - marginal_cost=np.where( - is_nonrenewable, - pumping_cost_bnusd_per_mm3, - WATER_MERIT_ORDER_EPSILON * marginal_cf, - ), + efficiency2=marginal_cf, + marginal_cost=WATER_MERIT_ORDER_EPSILON * marginal_cf, p_nom=tiers["capacity_mm3"].to_numpy(), p_nom_extendable=False, region=tiers["region"].to_numpy(), period=tiers["period"].astype(int).to_numpy(), - source=source, + source=tiers["source"].to_numpy(), ) # Accumulating impact stores: renewable water scarcity and non-renewable diff --git a/workflow/scripts/doc_figures/water_basin_availability.py b/workflow/scripts/doc_figures/water_basin_availability.py deleted file mode 100644 index 98e7b728..00000000 --- a/workflow/scripts/doc_figures/water_basin_availability.py +++ /dev/null @@ -1,165 +0,0 @@ -#!/usr/bin/env python3 -# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek -# -# SPDX-License-Identifier: GPL-3.0-or-later - -"""Generate basin water availability map for crop production documentation. - -Shows yearly average blue water availability by river basin (GRDC 405 basins). -""" - -import cartopy.crs as ccrs -import geopandas as gpd -import matplotlib.pyplot as plt -import pandas as pd - -from workflow.scripts.doc_figures_config import ( - COLORMAPS, - FIGURE_WIDTH, - FONT_SIZES, - apply_doc_style, - save_doc_figure, -) - - -def main( - basin_shapefile_path: str, - water_data_path: str, - svg_output_path: str, - png_output_path: str, -): - """Generate basin water availability map. - - Args: - basin_shapefile_path: Path to basin shapefile - water_data_path: Path to blue water availability CSV - svg_output_path: Path for output SVG file - png_output_path: Path for output PNG file - """ - # Apply documentation styling - apply_doc_style() - - # Load basin shapefile - basins = gpd.read_file(basin_shapefile_path) - - # Ensure CRS is WGS84 - if basins.crs is None: - basins = basins.set_crs(4326, allow_override=True) - else: - basins = basins.to_crs(4326) - - # Load water availability data - water_data = pd.read_csv(water_data_path) - - # Calculate yearly average water availability per basin - yearly_avg = ( - water_data.groupby("basin_id") - .agg({"blue_water_availability_m3": "sum", "area_km2": "first"}) - .reset_index() - ) - yearly_avg.columns = ["basin_id", "annual_water_m3", "area_km2"] - - # Convert to mm/year for better spatial comparison - # mm = m³ / (km² * 1e6 m²/km²) * 1000 mm/m = m³ / (km² * 1000) - yearly_avg["annual_water_mm"] = yearly_avg["annual_water_m3"] / ( - yearly_avg["area_km2"] * 1000 - ) - - # Merge with basins (assuming basin shapefile has a matching ID field) - # Common field names: BASIN_ID, BasinID, ID, FID - # Let's check what the shapefile has - basin_id_col = None - for col in ["BASIN_ID", "BasinID", "ID", "FID", "basin_id"]: - if col in basins.columns: - basin_id_col = col - break - - if basin_id_col is None: - # Fallback: use index + 1 - basins["basin_id"] = basins.index + 1 - basin_id_col = "basin_id" - - basins = basins.merge( - yearly_avg, left_on=basin_id_col, right_on="basin_id", how="left" - ) - - # Create figure with EqualEarth projection - fig, ax = plt.subplots( - figsize=(FIGURE_WIDTH, FIGURE_WIDTH * 0.5), - subplot_kw={"projection": ccrs.EqualEarth()}, - ) - - ax.set_global() - ax.set_facecolor("white") - - # Plot basin water availability - basins.plot( - column="annual_water_mm", - ax=ax, - cmap=COLORMAPS["water"], - edgecolor="white", - linewidth=0.2, - transform=ccrs.PlateCarree(), - legend=False, - missing_kwds={"color": "#e0e0e0", "alpha": 0.3}, - ) - - # Add coastlines for context - ax.coastlines(linewidth=0.3, color="#888888", alpha=0.3) - - # Style the map frame - for name, spine in ax.spines.items(): - if name == "geo": - spine.set_visible(True) - spine.set_linewidth(0.5) - spine.set_edgecolor("#555555") - spine.set_alpha(0.7) - else: - spine.set_visible(False) - - # Add colorbar - sm = plt.cm.ScalarMappable( - cmap=COLORMAPS["water"], - norm=plt.Normalize( - vmin=basins["annual_water_mm"].min(), - vmax=basins["annual_water_mm"].quantile(0.95), # Cap at 95th percentile - ), - ) - sm.set_array([]) - cbar = plt.colorbar(sm, ax=ax, orientation="horizontal", pad=0.05, fraction=0.046) - cbar.set_label( - "Annual Water Availability (mm/year)", fontsize=FONT_SIZES["colorbar_label"] - ) - - # Add data source note - ax.text( - 0.02, - 0.02, - "Data: Water Footprint Network (GRDC 405 basins)", - transform=ax.transAxes, - fontsize=FONT_SIZES["colorbar_tick"], - verticalalignment="bottom", - bbox={ - "boxstyle": "round,pad=0.3", - "facecolor": "white", - "alpha": 0.7, - "edgecolor": "none", - }, - ) - - plt.tight_layout() - - # Save SVG and PNG - save_doc_figure(fig, svg_output_path, format="svg") - save_doc_figure(fig, png_output_path, format="png", dpi=300) - plt.close(fig) - - -if __name__ == "__main__": - # Snakemake integration - main( - basin_shapefile_path=snakemake.input.basin_shapefile, - water_data_path=snakemake.input.water_data, - svg_output_path=snakemake.output.svg, - png_output_path=snakemake.output.png, - ) diff --git a/workflow/scripts/process_blue_water_availability.py b/workflow/scripts/process_blue_water_availability.py deleted file mode 100644 index 00f679f0..00000000 --- a/workflow/scripts/process_blue_water_availability.py +++ /dev/null @@ -1,108 +0,0 @@ -""" -SPDX-FileCopyrightText: 2026 Koen van Greevenbroek - -SPDX-License-Identifier: GPL-3.0-or-later -""" - -from pathlib import Path - -import geopandas as gpd -import pandas as pd - -MONTH_COLUMNS = [ - "Jan", - "Feb", - "Mar", - "Apr", - "May", - "Jun", - "Jul", - "Aug", - "Sep", - "Oct", - "Nov", - "Dec", -] - -MM3_TO_M3 = 1e6 - - -def _read_excel_availability(path: str) -> pd.DataFrame: - raw = pd.read_excel(path, sheet_name="Appendix-VII", header=2) - # Expect first row to contain column group names, second row month names - if raw.empty: - raise ValueError("Appendix-VII sheet appears empty") - - month_row = raw.iloc[1] - base_row = raw.iloc[0] - - base_row.iloc[0] - base_row.iloc[1] - - months = month_row.iloc[2:14].tolist() - if any(not isinstance(m, str) for m in months): - raise ValueError("Could not read month names from Appendix-VII") - - months = [m.strip().title()[:3] for m in months] - if months != MONTH_COLUMNS: - raise ValueError("Unexpected month order in Appendix-VII: " + ", ".join(months)) - - data = raw.iloc[2:].copy() - data = data.rename( - columns={ - base_row.index[0]: "basin_id", - base_row.index[1]: "basin_name", - } - ) - # Rename month columns by exact name mapping - for idx, month in enumerate(MONTH_COLUMNS, start=2): - col = raw.columns[idx] - data = data.rename(columns={col: month}) - - avg_col = raw.columns[14] - data = data.rename(columns={avg_col: "Average"}) - - data = data.dropna(subset=["basin_id"]).copy() - data["basin_id"] = data["basin_id"].astype(int) - return data[["basin_id", "basin_name", *MONTH_COLUMNS, "Average"]] - - -if __name__ == "__main__": - shapefile: str = snakemake.input.shapefile # type: ignore[name-defined] - excel_path: str = snakemake.input.excel # type: ignore[name-defined] - output_csv: str = snakemake.output[0] # type: ignore[name-defined] - - basin_gdf = gpd.read_file(shapefile)[ - ["BASIN_ID", "DRAINAGE", "AREA_CALC", "Population"] - ] - basin_gdf = basin_gdf.rename( - columns={ - "BASIN_ID": "basin_id", - "DRAINAGE": "drainage", - "AREA_CALC": "area_km2", - "Population": "population", - } - ) - - excel_df = _read_excel_availability(excel_path) - - merged = basin_gdf.merge(excel_df, on="basin_id", how="left") - if merged[MONTH_COLUMNS].isna().any().any(): - missing = merged[merged[MONTH_COLUMNS].isna().any(axis=1)]["basin_id"].tolist() - raise ValueError( - "Missing monthly availability for basins: " - + ", ".join(str(b) for b in missing[:10]) - ) - - records = [] - for month_idx, month in enumerate(MONTH_COLUMNS, start=1): - month_values = merged[["basin_id", "drainage", "area_km2", "population"]].copy() - month_values["month"] = month_idx - month_values["blue_water_availability_m3"] = merged[month] * MM3_TO_M3 - records.append(month_values) - - out_df = pd.concat(records, ignore_index=True) - out_df = out_df.sort_values(["basin_id", "month"]).reset_index(drop=True) - - Path(output_csv).parent.mkdir(parents=True, exist_ok=True) - out_df.to_csv(output_csv, index=False) diff --git a/workflow/scripts/solve_model/core.py b/workflow/scripts/solve_model/core.py index a93b7676..5f72b067 100644 --- a/workflow/scripts/solve_model/core.py +++ b/workflow/scripts/solve_model/core.py @@ -709,6 +709,11 @@ def add_water_scarcity_cap(n: pypsa.Network, cap_mm3_world_eq: float) -> None: The water-scarcity store is extendable, so bounding its capacity bounds the accumulated scarcity (Mm^3 world-equivalent). Used to trace the emissions vs water-scarcity Pareto front by sweeping the cap while pricing GHG. + + Non-renewable groundwater carries no CF and does not count against this + cap; with ``water.supply.groundwater`` on, combine the cap with a + groundwater_depletion price or cap, or the LP can satisfy it by mining + (``run_solve`` warns in that case). """ n.stores.static.at["store:impact:water_scarcity", "e_nom_max"] = cap_mm3_world_eq @@ -1528,11 +1533,24 @@ def run_solve( "water.supply.groundwater: without the groundwater bands nothing " "is ever mined, so the lever is vacuous." ) + if ( + scarcity_capped + and smk.params.water_groundwater + and not (scarcity_priced or depletion_priced or depletion_capped) + ): + # nonrenewable_cf charges mining only under scarcity *pricing*; a bare + # cap leaves CF-free mining as an escape deterred only by pumping cost. + logger.warning( + "water_scarcity cap is porous: groundwater mining carries no CF and " + "is neither priced nor capped, so the LP can meet the cap by " + "substituting mined groundwater. Combine with a " + "groundwater_depletion price or cap for a closed sweep." + ) if depletion_priced: add_groundwater_depletion_pricing_to_objective( n, float(smk.params.groundwater_price) ) - if smk.params.groundwater_cap is not None: + if depletion_capped: add_groundwater_depletion_cap(n, float(smk.params.groundwater_cap)) # Update health store marginal costs to match scenario value_per_yll. diff --git a/workflow/scripts/water_periods.py b/workflow/scripts/water_periods.py index 261e90b0..1cde7085 100644 --- a/workflow/scripts/water_periods.py +++ b/workflow/scripts/water_periods.py @@ -102,14 +102,15 @@ def calendar_period_shares( start_days: np.ndarray, length_days: np.ndarray, water_periods: int, -) -> np.ndarray: +) -> tuple[np.ndarray, np.ndarray]: """``(N, T)`` period demand shares from an observed monthly calendar. ``monthly_shares`` is ``(N, 12)`` of observed per-month growing-area shares (MIRCA-OS). Rows with a positive monthly profile are binned into the ``T`` periods; rows that are all-zero (no observed calendar for that season) fall back to the GAEZ growing-season split ``period_demand_shares(start, length)``. - Fully vectorised. + Fully vectorised. Returns the shares and the ``(N,)`` boolean mask of rows + with an observed calendar. """ periods = int(water_periods) monthly = np.asarray(monthly_shares, dtype=float) From be97646bcab77d4729901fa43b304ae26ef72722 Mon Sep 17 00:00:00 2001 From: Koen van Greevenbroek Date: Wed, 22 Jul 2026 12:00:56 -0700 Subject: [PATCH 07/12] fix: emit the current_use water tier table The water restructure declared region_water_tiers.csv as an output of build_region_water_current_use and documented the current_use source as a single zero-CF tier, but process_huang_irrigation_water.py never wrote the file. Nothing exercised the path -- the test config uses aware -- until cost calibration (which pins availability: current_use) failed on the missing output. Write the flat zero-scarcity tier per region-month in the shared schema. --- .../scripts/process_huang_irrigation_water.py | 21 +++++++++++++++---- 1 file changed, 17 insertions(+), 4 deletions(-) diff --git a/workflow/scripts/process_huang_irrigation_water.py b/workflow/scripts/process_huang_irrigation_water.py index 763f1fe5..53454aad 100644 --- a/workflow/scripts/process_huang_irrigation_water.py +++ b/workflow/scripts/process_huang_irrigation_water.py @@ -6,11 +6,12 @@ Aggregates monthly gridded irrigation water withdrawal, stored as a depth (mm/month at 0.5 degree resolution), converting it to a volume via grid-cell -area before aggregating to model regions. Produces outputs compatible with the -sustainable water availability data from the Water Footprint Network. +area before aggregating to model regions. -This script produces the availability tables in the shared schema -so that the two data sources can be used interchangeably. +This script produces the availability tables in the shared schema of the +"aware" source, including a single zero-CF supply tier per region-month that +reproduces a hard cap at current withdrawals, so the two availability sources +can be used interchangeably. Reference: Huang et al. (2018). Reconstruction of global gridded monthly sectoral @@ -480,6 +481,7 @@ def process_huang_irrigation( monthly_out: str = snakemake.output.monthly_region # type: ignore[name-defined] growing_out: str = snakemake.output.region_growing # type: ignore[name-defined] + tiers_out: str = snakemake.output.tiers # type: ignore[name-defined] monthly_df, growing_df = process_huang_irrigation( nc_path, regions_path, crop_files, reference_year @@ -490,3 +492,14 @@ def process_huang_irrigation( Path(growing_out).parent.mkdir(parents=True, exist_ok=True) growing_df.to_csv(growing_out, index=False) + + # One flat zero-scarcity tier per region-month: a hard availability cap at + # current withdrawals, in the same schema as the AWARE scarcity curve + # (capacity in Mm3; compose_water_supply converts it to consumption basis). + tiers_df = monthly_df[monthly_df["water_available_m3"] > 0].copy() + tiers_df["tier"] = 0 + tiers_df["capacity_mm3"] = tiers_df["water_available_m3"] * 1e-6 + tiers_df["marginal_cf"] = 0.0 + tiers_df[["region", "month", "tier", "capacity_mm3", "marginal_cf"]].to_csv( + tiers_out, index=False + ) From abf3e3c67db692797b6e0286ee0fbcec023a9cd0 Mon Sep 17 00:00:00 2001 From: Koen van Greevenbroek Date: Wed, 22 Jul 2026 12:57:40 -0700 Subject: [PATCH 08/12] chore: recalibrate both artefact sets for the water restructure Full tools/calibrate chains against the restructured water system (default set from config/default.yaml, gbd-anchored from config/gsa.yaml). The previous stamps recorded the pre-restructure water keys, so every non-test run failed the provenance check. Stability converged in 3 (default) / 4 (gbd-anchored) Broyden iterations with L1 costs near the previous centres -- default: cropland 1.47, grassland 0.24, feed 0.062; gbd-anchored: cropland 1.62, grassland 0.24, feed 0.056 -- so no residual supply/demand mismatch is hiding in the penalty. --- .../calibration/default/animal_cost.csv | 2450 ++-- .../curated/calibration/default/crop_cost.csv | 11348 +++++++-------- .../default/deviation_penalty.yaml | 10 +- .../calibration/default/exogenous_feed.csv | 183 +- .../calibration/default/exogenous_forage.csv | 28 +- .../calibration/default/fodder_conversion.csv | 16 +- .../calibration/default/food_demand.csv | 62 +- .../calibration/default/food_waste.yaml | 22 +- .../calibration/default/grassland_cost.csv | 348 +- .../calibration/default/grassland_yield.csv | 226 +- .../calibration/default/multi_crop_cost.csv | 925 +- .../calibration/default/provenance.yaml | 23 +- .../calibration/gbd-anchored/animal_cost.csv | 2450 ++-- .../calibration/gbd-anchored/crop_cost.csv | 11400 ++++++++-------- .../gbd-anchored/deviation_penalty.yaml | 10 +- .../gbd-anchored/exogenous_feed.csv | 173 +- .../gbd-anchored/exogenous_forage.csv | 28 +- .../gbd-anchored/fodder_conversion.csv | 16 +- .../calibration/gbd-anchored/food_demand.csv | 60 +- .../calibration/gbd-anchored/food_waste.yaml | 22 +- .../gbd-anchored/grassland_cost.csv | 348 +- .../gbd-anchored/grassland_yield.csv | 226 +- .../gbd-anchored/multi_crop_cost.csv | 925 +- .../calibration/gbd-anchored/provenance.yaml | 23 +- 24 files changed, 15681 insertions(+), 15641 deletions(-) diff --git a/data/curated/calibration/default/animal_cost.csv b/data/curated/calibration/default/animal_cost.csv index b64c463f..6938d8cc 100644 --- a/data/curated/calibration/default/animal_cost.csv +++ b/data/curated/calibration/default/animal_cost.csv @@ -1,1226 +1,1226 @@ product,country,correction_bnusd_per_mt_feed -dairy,AFG,0.21505843102931976 -dairy,AGO,-0.1876072809100151 -dairy,ALB,0.13552791625261307 -dairy,ARE,0.06280056573450565 -dairy,ARG,0.011342272162437439 -dairy,ARM,0.16288457810878754 -dairy,ASM,0.0 -dairy,ATG,-0.04762404132634401 -dairy,AUS,0.07240412384271622 -dairy,AUT,0.3095840662717819 -dairy,AZE,0.17925383895635605 -dairy,BDI,-0.08750808797776699 -dairy,BEL,0.31986103951931 -dairy,BEN,-0.16715409606695175 -dairy,BFA,-0.1901455521583557 -dairy,BGD,0.055752700194716454 -dairy,BGR,0.3674655854701996 -dairy,BHS,0.006858016364276409 -dairy,BIH,0.5480914413928986 -dairy,BLR,0.25135288387537 -dairy,BLZ,-0.0775703489780426 -dairy,BOL,-0.11510015651583672 -dairy,BRA,-0.04694885388016701 -dairy,BRB,-0.252767950296402 -dairy,BRN,0.011835694313049316 -dairy,BTN,0.05094238743185997 -dairy,BWA,-0.09379293583333492 -dairy,CAF,-0.12592153623700142 -dairy,CAN,-0.026900801807641983 -dairy,CHE,0.3183267265558243 -dairy,CHL,-0.005784417735412717 -dairy,CHN,0.40335820615291595 -dairy,CIV,-0.17336510866880417 -dairy,CMR,-0.17985638231039047 -dairy,COD,-0.19002040475606918 -dairy,COG,-0.10799645259976387 -dairy,COL,-0.06486621871590614 -dairy,COM,-0.10457734391093254 -dairy,CPV,-0.37824274599552155 -dairy,CRI,0.0 -dairy,CUB,-0.1465173289179802 -dairy,CYP,0.16505001857876778 -dairy,CZE,0.2932858467102051 -dairy,DEU,0.3284545838832855 -dairy,DJI,-0.016560524702072144 -dairy,DNK,0.38501209020614624 -dairy,DOM,-0.015604079701006413 -dairy,DZA,0.1519153192639351 -dairy,ECU,-0.06757636927068233 -dairy,EGY,-0.20971631072461605 -dairy,ERI,-0.07563293725252151 -dairy,ESP,0.17928596585988998 -dairy,EST,0.39216428995132446 -dairy,ETH,-0.10038527473807335 -dairy,FIN,0.4443316012620926 -dairy,FJI,0.07324349880218506 -dairy,FRA,0.20888175070285797 -dairy,GAB,-0.011362389661371708 -dairy,GBR,0.32480472326278687 -dairy,GEO,0.16491908580064774 -dairy,GHA,-0.09652552008628845 -dairy,GIN,-0.17122869193553925 -dairy,GMB,-0.12777040526270866 -dairy,GNB,-0.35606179386377335 -dairy,GNQ,0.0 -dairy,GRC,0.22036242485046387 -dairy,GRD,-0.11858516186475754 -dairy,GTM,-0.09408999793231487 -dairy,GUF,0.0 -dairy,GUY,-0.0320122396806255 -dairy,HND,-0.03260217234492302 -dairy,HRV,0.24321743845939636 -dairy,HTI,-0.014154321514070034 -dairy,HUN,0.3232627511024475 -dairy,IDN,0.1092676930129528 -dairy,IND,-0.24345862679183483 -dairy,IRL,0.21396613121032715 -dairy,IRN,0.24245232343673706 -dairy,IRQ,-0.00023535825312137604 -dairy,ISL,0.3173813670873642 -dairy,ISR,0.18669521808624268 -dairy,ITA,0.2586148828268051 -dairy,JAM,-0.03720374405384064 -dairy,JOR,0.4034206122159958 -dairy,JPN,0.37163418531417847 -dairy,KAZ,0.3036366403102875 -dairy,KEN,-0.04472518665716052 -dairy,KGZ,0.45697785913944244 -dairy,KHM,-0.028660813346505165 -dairy,KOR,0.3826141655445099 -dairy,LAO,0.09088078700006008 -dairy,LBN,0.3858019560575485 -dairy,LBR,-0.05144108831882477 -dairy,LBY,0.06293094903230667 -dairy,LKA,0.16912438720464706 -dairy,LSO,-0.03761456394568086 -dairy,LTU,0.4023910015821457 -dairy,LUX,0.27816079556941986 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+meat-sheep,ZAF,-0.028218806255608797 +meat-sheep,ZMB,-0.06696736067533493 +meat-sheep,ZWE,-0.366745226085186 diff --git a/data/curated/calibration/default/crop_cost.csv b/data/curated/calibration/default/crop_cost.csv index 1b36b8c2..0490df12 100644 --- a/data/curated/calibration/default/crop_cost.csv +++ b/data/curated/calibration/default/crop_cost.csv @@ -1,304 +1,303 @@ crop,country,correction_bnusd_per_mha -alfalfa,AFG,-0.3750787377357483 -alfalfa,AGO,-0.2651873230934143 -alfalfa,ALB,-0.004321811720728874 -alfalfa,ARE,7.5 -alfalfa,ARG,-0.31196562945842743 -alfalfa,ARM,0.0 -alfalfa,ATG,0.0 -alfalfa,AUS,0.0 -alfalfa,AUT,-0.7556191384792328 -alfalfa,AZE,0.814435601234436 -alfalfa,BDI,-0.8987658023834229 -alfalfa,BEL,-0.23728060722351074 -alfalfa,BEN,0.821510910987854 -alfalfa,BFA,0.0 -alfalfa,BGD,-0.7333669364452362 -alfalfa,BGR,1.0534589290618896 -alfalfa,BHS,-0.5689958930015564 -alfalfa,BIH,0.8138508796691895 -alfalfa,BLR,0.13893422484397888 -alfalfa,BLZ,-0.5492207109928131 -alfalfa,BOL,-0.22297222912311554 -alfalfa,BRA,-0.44687044620513916 -alfalfa,BRB,2.6721359491348267 -alfalfa,BRN,1.4280236959457397 -alfalfa,BTN,0.09801395237445831 -alfalfa,BWA,0.0 -alfalfa,CAF,-0.6585278213024139 -alfalfa,CAN,-0.1303732693195343 -alfalfa,CHE,0.5097993761301041 -alfalfa,CHL,-0.6260356903076172 -alfalfa,CHN,0.5983059406280518 +alfalfa,AFG,-0.3613618314266205 +alfalfa,AGO,-0.9381504952907562 +alfalfa,ALB,-0.21057342505082488 +alfalfa,ARE,0.8991113305091858 +alfalfa,ARG,-0.4150523543357849 +alfalfa,ARM,-0.0 +alfalfa,ATG,-7.5 +alfalfa,AUS,1.0110264420509338 +alfalfa,AUT,-0.8089405298233032 +alfalfa,AZE,0.4651461988687515 +alfalfa,BDI,-0.8992443978786469 +alfalfa,BEL,0.045041752979159355 +alfalfa,BEN,0.49790994822978973 +alfalfa,BFA,-0.0 +alfalfa,BGD,1.3732854723930359 +alfalfa,BGR,0.2313733473420143 +alfalfa,BHS,-4.105992168188095 +alfalfa,BIH,3.9570419043302536 +alfalfa,BLR,-2.5428788363933563 +alfalfa,BLZ,-0.010105629451572895 +alfalfa,BOL,-0.33576732873916626 +alfalfa,BRA,-0.653052031993866 +alfalfa,BRB,2.0795974731445312 +alfalfa,BRN,-0.8346583247184753 +alfalfa,BTN,-0.2713441401720047 +alfalfa,BWA,-7.5 +alfalfa,CAF,-0.9728804230690002 +alfalfa,CAN,-0.5801125466823578 +alfalfa,CHE,0.49974900484085083 +alfalfa,CHL,-0.8672170341014862 +alfalfa,CHN,0.10282701998949051 alfalfa,CIV,7.5 -alfalfa,CMR,1.7329913973808289 -alfalfa,COD,-0.1673155128955841 -alfalfa,COG,-0.9112309217453003 -alfalfa,COL,-0.8513142168521881 -alfalfa,COM,0.6860150396823883 -alfalfa,CPV,-0.4042091369628906 -alfalfa,CRI,-0.2579779550433159 -alfalfa,CUB,-0.28110775351524353 -alfalfa,CYP,-7.5 -alfalfa,CZE,0.33479776978492737 -alfalfa,DEU,0.1509365662932396 -alfalfa,DJI,1.603806734085083 -alfalfa,DNK,7.5 -alfalfa,DOM,-0.8691022992134094 -alfalfa,DZA,-0.07842564582824707 -alfalfa,ECU,-0.12939461693167686 -alfalfa,EGY,-0.260550320148468 -alfalfa,ERI,-0.8744806945323944 -alfalfa,ESP,-1.0377808809280396 -alfalfa,EST,0.0 -alfalfa,ETH,-0.8274461328983307 -alfalfa,FIN,0.0 -alfalfa,FJI,0.13141752034425735 -alfalfa,FRA,-0.33104005455970764 -alfalfa,GAB,-0.746298760175705 -alfalfa,GBR,0.17851915955543518 -alfalfa,GEO,-0.573031097650528 -alfalfa,GHA,7.5 -alfalfa,GIN,-0.387450709939003 -alfalfa,GMB,-0.25826479494571686 -alfalfa,GNB,0.0 -alfalfa,GNQ,-0.7589189112186432 -alfalfa,GRC,-0.650564581155777 -alfalfa,GRD,0.4752156436443329 -alfalfa,GTM,-0.522682249546051 -alfalfa,GUF,-2.706025242805481 -alfalfa,GUY,-0.07720011845231056 -alfalfa,HND,-0.34651389718055725 -alfalfa,HRV,-1.71925288438797 -alfalfa,HTI,-0.8191313445568085 -alfalfa,HUN,-2.167660281062126 -alfalfa,IDN,0.04110857471823692 -alfalfa,IND,0.0 -alfalfa,IRL,-0.5411296486854553 -alfalfa,IRN,-1.204572081565857 -alfalfa,IRQ,2.765779972076416 -alfalfa,ISL,-0.7303893268108368 -alfalfa,ISR,1.7266695499420166 -alfalfa,ITA,0.23923970758914948 -alfalfa,JAM,2.2525503635406494 -alfalfa,JOR,4.008713722229004 -alfalfa,JPN,0.017270792406634428 -alfalfa,KAZ,-0.5172078013420105 -alfalfa,KEN,-0.43167561292648315 -alfalfa,KGZ,-0.8171102404594421 -alfalfa,KHM,-0.6746552288532257 -alfalfa,KOR,0.08994222432374954 -alfalfa,LAO,-0.315351665019989 -alfalfa,LBN,2.3072054386138916 -alfalfa,LBR,-0.8882774710655212 -alfalfa,LBY,-0.10145418345928192 -alfalfa,LKA,-0.022444549947977066 -alfalfa,LSO,-0.8092021346092224 -alfalfa,LTU,0.3543912172317505 -alfalfa,LUX,-0.3182141184806824 -alfalfa,LVA,-0.5082782655954361 -alfalfa,MAR,2.8600780963897705 -alfalfa,MDA,2.4347543716430664 -alfalfa,MDG,-0.04350392799824476 -alfalfa,MEX,0.0 -alfalfa,MKD,1.373655080795288 -alfalfa,MLI,-0.9638939499855042 -alfalfa,MLT,-3.76202882360667 -alfalfa,MMR,0.8664765655994415 -alfalfa,MNE,1.513783574104309 -alfalfa,MNG,-0.7835801839828491 -alfalfa,MOZ,-0.5750786066055298 -alfalfa,MRT,-7.5 -alfalfa,MUS,-0.7995986044406891 -alfalfa,MWI,-0.5677796304225922 -alfalfa,MYS,-0.34928590059280396 -alfalfa,NAM,-0.29255859553813934 -alfalfa,NER,0.0 -alfalfa,NGA,-0.9353777766227722 -alfalfa,NIC,-1.859633982181549 -alfalfa,NLD,6.3248326778411865 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-alfalfa,TCD,-0.894737184047699 -alfalfa,TGO,-0.3819449692964554 -alfalfa,THA,-0.673568457365036 -alfalfa,TJK,-0.09686601161956787 -alfalfa,TKM,-0.8394297957420349 -alfalfa,TLS,-0.3208562284708023 -alfalfa,TTO,-0.7717661559581757 -alfalfa,TUN,-2.9879140853881836 -alfalfa,TUR,4.244081735610962 -alfalfa,TWN,1.0885874032974243 -alfalfa,TZA,-0.3090435713529587 -alfalfa,UGA,-0.917542427778244 -alfalfa,UKR,-0.04196452349424362 -alfalfa,URY,-0.005964932031929493 -alfalfa,USA,0.15973469614982605 -alfalfa,UZB,-1.349977970123291 -alfalfa,VEN,-0.27461254596710205 -alfalfa,VNM,0.3924734741449356 -alfalfa,VUT,0.12362154200673103 -alfalfa,YEM,-0.6437023878097534 -alfalfa,ZAF,-0.39307543635368347 -alfalfa,ZMB,-0.3911762535572052 -alfalfa,ZWE,0.0 -apple,AFG,0.0 -apple,ALB,0.0 -apple,ARG,0.0 -apple,ARM,0.0 -apple,AUS,0.0 -apple,AUT,0.0 -apple,AZE,0.0 -apple,BEL,0.0 -apple,BGR,0.0 -apple,BIH,0.0 -apple,BLR,0.0 -apple,BLZ,0.0 -apple,BOL,0.0 -apple,BRA,0.0 -apple,BTN,0.0 -apple,BWA,0.0 -apple,CAN,0.0 -apple,CHE,0.0 -apple,CHL,0.0 -apple,CHN,0.0 -apple,COL,0.0 -apple,CZE,0.0 -apple,DEU,0.0 -apple,DNK,0.0 -apple,DZA,0.0 -apple,ECU,0.0 -apple,EGY,0.0 -apple,ESP,0.0 -apple,EST,0.0 -apple,ETH,0.0 -apple,FIN,0.0 -apple,FRA,0.0 -apple,GBR,0.0 -apple,GEO,0.0 -apple,GRC,0.0 -apple,GTM,0.0 -apple,HND,0.0 -apple,HRV,0.0 -apple,HUN,0.0 -apple,IND,0.0 -apple,IRL,0.0 -apple,IRN,0.0 -apple,IRQ,0.0 -apple,ISR,0.0 -apple,ITA,0.0 -apple,JOR,0.0 -apple,JPN,0.0 -apple,KAZ,0.0 -apple,KEN,0.0 -apple,KGZ,0.0 -apple,KOR,0.0 -apple,LAO,0.0 -apple,LBN,0.0 -apple,LBY,0.0 -apple,LSO,0.0 -apple,LTU,0.0 -apple,LUX,0.0 -apple,LVA,0.0 -apple,MAR,0.0 -apple,MDA,0.0 -apple,MDG,-1.9327446222305298 -apple,MEX,0.0 -apple,MKD,0.0 -apple,MMR,0.0 -apple,MNE,0.0 -apple,MNG,0.0 -apple,MOZ,0.0 -apple,NIC,0.0 -apple,NLD,0.0 -apple,NOR,0.0 -apple,NPL,0.0 -apple,NZL,0.0 -apple,PAK,0.0 -apple,PER,0.0 -apple,POL,0.0 -apple,PRT,0.0 -apple,PRY,0.0 -apple,PSE,0.0 -apple,ROU,0.0 -apple,RUS,0.0 +alfalfa,CMR,0.5399894714355469 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-sunflower,ETH,-0.18067964911460876 -sunflower,FIN,0.0 -sunflower,FRA,-0.03382093319669366 -sunflower,GEO,-0.10053089261054993 -sunflower,GRC,-0.3584790676832199 -sunflower,GUF,0.0 -sunflower,GUY,0.0 -sunflower,HRV,-2.9446407854557037 -sunflower,HUN,-1.1685201525688171 -sunflower,IND,-1.2177455425262451 -sunflower,IRN,-0.38291820883750916 -sunflower,IRQ,-0.05899104103446007 -sunflower,ISR,-3.3506226539611816 -sunflower,ITA,-0.03892996534705162 +sunflower,CZE,-0.07317560911178589 +sunflower,DEU,-0.03251445759087801 +sunflower,DNK,-0.0 +sunflower,DZA,0.07130832597613335 +sunflower,ECU,-0.36056022346019745 +sunflower,EGY,-0.33548668026924133 +sunflower,ERI,-0.0 +sunflower,ESP,-0.14190415292978287 +sunflower,EST,-0.22311664372682571 +sunflower,ETH,-0.21693839132785797 +sunflower,FIN,-0.0 +sunflower,FRA,-0.27413320541381836 +sunflower,GEO,-0.1343516707420349 +sunflower,GRC,-0.49574409425258636 +sunflower,GUF,-0.0 +sunflower,GUY,-0.0 +sunflower,HRV,-4.0307208597660065 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-sunflower,PAK,-0.20527204871177673 -sunflower,PER,0.0 -sunflower,POL,0.014236611314117908 -sunflower,PRT,0.11160717159509659 -sunflower,PRY,-0.29477767646312714 -sunflower,PSE,0.5536826848983765 -sunflower,ROU,0.16893070936203003 -sunflower,RUS,0.31876906752586365 -sunflower,RWA,-0.498375728726387 -sunflower,SDN,-0.6549960374832153 -sunflower,SOM,0.0 -sunflower,SRB,-0.18521017208695412 -sunflower,SSD,-0.24161150306463242 -sunflower,SUR,0.0 -sunflower,SVK,-0.10248231887817383 -sunflower,SVN,0.324223130941391 -sunflower,SWZ,-0.24153606593608856 -sunflower,SYR,-0.32135921716690063 -sunflower,TCD,0.0 -sunflower,THA,-0.12115718051791191 -sunflower,TJK,3.873677112162113 -sunflower,TKM,0.0 -sunflower,TUN,-0.4605240523815155 -sunflower,TUR,-0.49767348170280457 -sunflower,TWN,0.9871941804885864 -sunflower,TZA,0.10896173119544983 -sunflower,UGA,-0.45247094333171844 -sunflower,UKR,-0.2528715431690216 -sunflower,URY,-0.16696994751691818 -sunflower,USA,0.24020259082317352 -sunflower,UZB,-0.661681592464447 -sunflower,VEN,-0.5007883161306381 -sunflower,VNM,0.0 -sunflower,ZAF,-0.12806689739227295 -sunflower,ZMB,-0.5842887759208679 -sunflower,ZWE,0.0021707266569137573 -sweet-potato,AFG,-2.874839663505554 -sweet-potato,AGO,-3.265822410583496 -sweet-potato,ALB,-2.316335678100586 -sweet-potato,ARE,-7.5 -sweet-potato,ARG,-3.881214737892151 -sweet-potato,ARM,-2.64909827709198 -sweet-potato,ATG,-4.378902435302734 -sweet-potato,AUS,0.0 -sweet-potato,AUT,-2.8073251247406006 -sweet-potato,AZE,-3.2629988193511963 -sweet-potato,BDI,-1.1382047533988953 -sweet-potato,BEL,-1.6622064709663391 -sweet-potato,BEN,-1.827791690826416 -sweet-potato,BFA,-3.6573729515075684 -sweet-potato,BGD,-0.2760493606328964 -sweet-potato,BGR,-2.668501377105713 -sweet-potato,BHS,-3.818389654159546 -sweet-potato,BIH,-3.1926116943359375 -sweet-potato,BLR,-4.5566500425338745 -sweet-potato,BLZ,-3.530309557914734 -sweet-potato,BOL,-0.7056885361671448 -sweet-potato,BRA,-2.7425594329833984 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-sweet-potato,CHE,-1.942561149597168 -sweet-potato,CHL,-1.62930166721344 +sweet-potato,BTN,7.5 +sweet-potato,BWA,-0.0 +sweet-potato,CAF,-3.8880326747894287 +sweet-potato,CAN,-3.1307886838912964 +sweet-potato,CHE,-2.1317754983901978 +sweet-potato,CHL,-7.5 sweet-potato,CHN,-7.5 -sweet-potato,CIV,-0.5139818489551544 -sweet-potato,CMR,-3.4078577756881714 -sweet-potato,COD,-3.6668689250946045 -sweet-potato,COG,-3.492530107498169 -sweet-potato,COL,-3.210129976272583 -sweet-potato,COM,-3.7614006996154785 +sweet-potato,CIV,-0.5126045346260071 +sweet-potato,CMR,-3.449243664741516 +sweet-potato,COD,-3.6700280904769897 +sweet-potato,COG,-3.4940396547317505 +sweet-potato,COL,-3.262499213218689 +sweet-potato,COM,-3.7716764211654663 sweet-potato,CPV,-7.5 -sweet-potato,CRI,3.211240231990814 -sweet-potato,CUB,-3.5258588790893555 -sweet-potato,CYP,-5.2983105182647705 -sweet-potato,CZE,-2.108704447746277 -sweet-potato,DEU,-1.4264941811561584 -sweet-potato,DNK,-1.9205120205879211 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-wheat,VNM,3.5684593468904495 -wheat,YEM,0.0 -wheat,ZAF,3.1975371837615967 -wheat,ZMB,4.39703106880188 -wheat,ZWE,2.800724744796753 -white-potato,AFG,-5.800930023193359 -white-potato,AGO,-6.625463962554932 -white-potato,ALB,-6.8373496532440186 -white-potato,ARE,-7.5 -white-potato,ARG,-7.239206075668335 -white-potato,ARM,-2.9537183046340942 -white-potato,ATG,-6.933290004730225 -white-potato,AUS,-7.5 -white-potato,AUT,-4.3990867137908936 -white-potato,AZE,-3.721527099609375 -white-potato,BDI,-2.2032004594802856 -white-potato,BEL,-2.27272891998291 +wheat,UKR,-0.3854474127292633 +wheat,URY,-0.15845760703086853 +wheat,USA,-0.27889737486839294 +wheat,UZB,-0.7637653946876526 +wheat,VEN,7.5 +wheat,VNM,7.5 +wheat,YEM,-0.5957063734531403 +wheat,ZAF,-0.345022976398468 +wheat,ZMB,0.28865188360214233 +wheat,ZWE,2.1602670550346375 +white-potato,AFG,-6.197274208068848 +white-potato,AGO,-6.8108296394348145 +white-potato,ALB,-7.185554265975952 +white-potato,ARE,-6.448779582977295 +white-potato,ARG,-7.5 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-yam,PAK,1.206875205039978 -yam,PAN,-3.1514991521835327 -yam,PER,-3.606115937232971 -yam,PHL,-1.6030202507972717 -yam,PNG,-2.5849483013153076 -yam,PRI,-7.476719617843628 +yam,KEN,-1.4882037043571472 +yam,KHM,-2.6322381496429443 +yam,LAO,0.9493604302406311 +yam,LBN,-4.759747624397278 +yam,LBR,-3.783653497695923 +yam,LKA,-3.0860074758529663 +yam,LSO,-0.0 +yam,MDG,-4.15936541557312 +yam,MEX,2.951883316040039 +yam,MLI,-5.260771751403809 +yam,MMR,-0.9187805652618408 +yam,MNE,-0.0 +yam,MOZ,-4.720427751541138 +yam,MRT,-4.346336841583252 +yam,MUS,-3.5727826356887817 +yam,MWI,1.796617031097412 +yam,MYS,-2.7936019897460938 +yam,NAM,-3.4631645679473877 +yam,NER,-5.156688213348389 +yam,NGA,-3.9387731552124023 +yam,NIC,2.7137115001678467 +yam,NPL,-5.065113067626953 +yam,PAK,-0.0 +yam,PAN,-3.2264866828918457 +yam,PER,-3.5559438467025757 +yam,PHL,-1.6469984650611877 +yam,PNG,-3.0244327783584595 +yam,PRI,-7.5 yam,PRY,7.5 -yam,PSE,-1.2889548242092133 -yam,RWA,-0.815086305141449 -yam,SAU,0.0 -yam,SDN,-5.722858905792236 -yam,SEN,-2.111696720123291 -yam,SLB,-1.7412835955619812 -yam,SLE,-5.370323419570923 -yam,SLV,2.908381938934326 -yam,SOM,0.0 -yam,SSD,-5.157663822174072 -yam,STP,-4.689535856246948 -yam,SUR,-2.0496089458465576 -yam,SWZ,-3.945995330810547 -yam,SYR,0.0 -yam,TCD,-4.010426998138428 -yam,TGO,-1.077474057674408 -yam,THA,-1.807403028011322 -yam,TLS,-5.455284118652344 +yam,PSE,-1.4629396200180054 +yam,RWA,-0.8948467671871185 +yam,SAU,-0.0 +yam,SDN,-5.548225402832031 +yam,SEN,-0.9069963693618774 +yam,SLB,-2.2544283866882324 +yam,SLE,-5.428421974182129 +yam,SLV,2.689927101135254 +yam,SOM,7.5 +yam,SSD,-5.277986526489258 +yam,STP,-4.694941759109497 +yam,SUR,-2.4590797424316406 +yam,SWZ,-4.03550910949707 +yam,SYR,-0.0 +yam,TCD,-4.677451848983765 +yam,TGO,-1.02513986825943 +yam,THA,-2.1230387687683105 +yam,TLS,-5.495773553848267 yam,TTO,-7.5 -yam,TWN,-2.3733160495758057 -yam,TZA,-4.54769492149353 -yam,UGA,-3.8769387006759644 -yam,URY,0.0 -yam,USA,5.996743202209473 -yam,VEN,-2.043738603591919 -yam,VNM,-3.632774829864502 -yam,VUT,-3.0644670724868774 -yam,ZAF,-4.348750352859497 -yam,ZMB,7.5 -yam,ZWE,-3.2570632696151733 +yam,TWN,-2.740014672279358 +yam,TZA,-4.686638355255127 +yam,UGA,-4.014009714126587 +yam,URY,-0.0 +yam,USA,7.5 +yam,VEN,-2.4075191020965576 +yam,VNM,-2.437577247619629 +yam,VUT,-3.4503121376037598 +yam,ZAF,-4.382262229919434 +yam,ZMB,-3.4753758907318115 +yam,ZWE,-3.3793407678604126 diff --git a/data/curated/calibration/default/deviation_penalty.yaml b/data/curated/calibration/default/deviation_penalty.yaml index 5455193f..177119fe 100644 --- a/data/curated/calibration/default/deviation_penalty.yaml +++ b/data/curated/calibration/default/deviation_penalty.yaml @@ -14,9 +14,9 @@ components: - grassland - feed l1_costs: - cropland: 2.281741945986492 - grassland: 0.16171905439400136 - feed: 0.10185911829217534 -iterations: 6 + cropland: 1.4668100847398033 + grassland: 0.23756434523520123 + feed: 0.06172639547529331 +iterations: 3 converged: true -final_residual_log_inf: 0.003938636151124174 +final_residual_log_inf: 0.018785337116737355 diff --git a/data/curated/calibration/default/exogenous_feed.csv b/data/curated/calibration/default/exogenous_feed.csv index b2f107dc..dcd11041 100644 --- a/data/curated/calibration/default/exogenous_feed.csv +++ b/data/curated/calibration/default/exogenous_feed.csv @@ -2,110 +2,109 @@ # # SPDX-License-Identifier: CC-BY-4.0 country,monogastric_protein_mt_dm,ruminant_protein_mt_dm,ruminant_roughage_mt_dm -AFG,0.021802,0.464259,6.454943 -AGO,0.0,0.0,0.935912 -ARE,0.0,0.0,1.41976 -ARM,0.0,0.0,0.921952 +AFG,0.0,0.513921,6.396201 +AGO,0.0,0.0,0.921706 +ARE,0.0,0.0,1.420038 +ARM,0.0,0.0,0.922057 ATG,0.0,0.0,0.001203 -AUS,0.207758,0.219236,0.0 -AZE,0.0,0.0,1.66954 -BFA,0.0,0.0,2.427717 -BGD,0.0,0.0,9.224991 +AUS,0.0,0.266946,0.0 +AZE,0.0,0.0,1.662357 +BFA,0.0,0.0,2.424819 +BGD,0.0,0.0,9.323274 BHS,0.0,0.0,0.000241 -BOL,0.0,0.0,3.061838 -BRA,0.0,0.0,4.291774 +BOL,0.0,0.0,3.079534 +BRA,0.0,0.0,3.789062 BRB,0.0,0.0,0.001186 -BRN,0.0,0.0,0.098264 -BTN,0.0,0.0,0.032692 -BWA,0.0,0.0,0.803647 -CAF,0.0,0.0,3.145706 -CHL,0.0,0.0,1.174771 -CHN,24.825367,8.937976,0.0 -CMR,0.0,0.0,1.961326 -COG,0.0,0.0,0.101832 -COL,0.0,0.0,4.967146 +BRN,0.03023,0.002684,0.098271 +BTN,0.0,0.0,0.032205 +BWA,0.0,0.0,0.801548 +CAF,0.0,0.0,3.139111 +CHL,0.0,0.0,1.159961 +CHN,36.765209,8.937976,0.0 +CMR,0.0,0.0,2.066831 +COG,0.0,0.0,0.101918 +COL,0.0,0.0,4.970007 COM,0.0,0.0,0.023564 CPV,0.0,0.0,0.014816 -CRI,0.0,0.0,0.475468 -CUB,0.0,0.0,0.474311 +CRI,0.0,0.0,0.477516 +CUB,0.0,0.0,0.474619 CYP,0.0,0.0,0.026106 DJI,0.0,0.0,0.253119 -DOM,0.0,0.0,0.732578 -DZA,0.0,0.0,7.00384 -ECU,0.0,0.0,0.999379 -EGY,0.0,0.0,7.622975 -ERI,0.0,0.0,0.647038 -ETH,0.0,0.0,3.538134 -FJI,0.025279,0.0,0.0 -GEO,0.0,0.0,0.291168 -GNB,0.0,0.0,0.082517 +DOM,0.0,0.0,0.733529 +DZA,0.0,0.0,6.986687 +ECU,0.0,0.0,0.999478 +EGY,0.0,0.0,7.620932 +ERI,0.0,0.0,0.647088 +ETH,0.0,0.0,3.690289 +FJI,0.025394,0.0,0.0 +GEO,0.0,0.0,0.295198 +GNB,0.0,0.0,0.082731 GRD,0.0,0.0,0.003329 -GTM,0.0,0.0,0.882024 -HND,0.0,0.0,0.328047 -HTI,0.0,0.0,0.56707 -IDN,3.118611,2.778974,0.0 -IND,0.0,0.0,289.459564 -IRL,0.0,0.0,0.334866 +GTM,0.0,0.0,0.901076 +HND,0.0,0.0,0.341193 +HTI,0.0,0.0,0.566936 +IDN,3.204576,2.778974,0.0 +IND,0.0,0.0,289.03186 +IRL,0.0,0.0,0.339502 ISL,0.0,0.0,0.030581 -ISR,0.0,0.0,1.114115 +ISR,0.0,0.0,1.114146 JAM,0.0,0.0,0.181489 -JOR,0.0,0.0,0.909527 -JPN,3.091849,0.0,1.321784 -KEN,0.0,0.0,8.557251 -KHM,0.165723,0.112118,0.0 -KOR,2.764511,0.374992,2.65805 -LAO,0.090297,0.071037,0.0 +JOR,0.0,0.0,0.909003 +JPN,1.923775,1.221458,1.328452 +KEN,0.0,0.0,8.594836 +KGZ,0.0,0.147937,0.0 +KHM,0.074614,0.208704,0.0 +KOR,2.713961,0.374992,2.659726 +LAO,0.084655,0.071492,0.0 LBN,0.0,0.0,0.255603 -LBY,0.0,0.0,0.793363 -LKA,0.045914,0.18201,0.0 -LSO,0.0,0.0,0.093721 -MAR,0.0,0.0,5.567743 -MEX,0.0,0.0,2.575333 +LBY,0.0,0.0,0.793866 +LKA,0.035028,0.18201,0.0 +LSO,0.0,0.0,0.092898 +MAR,0.0,0.0,5.582377 +MEX,0.0,0.0,2.590309 MLT,0.0,0.0,0.002164 -MMR,0.0,0.395933,0.0 -MNE,0.0,0.0,0.002303 -MNG,0.0,0.016502,15.397603 -MRT,0.0,0.0,2.106061 -MYS,0.0,0.0,0.202319 -NAM,0.0,0.0,0.693314 -NER,0.0,0.0,1.711404 -NIC,0.0,0.0,1.291721 -NLD,0.0,0.0,0.117066 -NPL,0.0,0.0,15.855499 -NZL,0.253462,1.020203,0.0 -OMN,0.0,0.0,1.601173 -PAK,0.551951,15.348308,166.856613 -PAN,0.0,0.0,0.335518 -PER,0.0,0.0,0.826841 -PHL,1.421447,0.288718,0.0 +MMR,0.0,0.46757,0.0 +MNE,0.0,0.0,0.002311 +MNG,0.0,0.002787,15.401712 +MRT,0.0,0.0,2.102427 +MYS,0.800147,0.24535,0.234748 +NAM,0.0,0.0,0.700566 +NER,0.0,0.0,1.710052 +NIC,0.0,0.0,1.29114 +NLD,0.0,0.0,0.127665 +NPL,0.0,0.0,15.811893 +NZL,0.17101,1.103938,0.0 +OMN,0.0,0.0,1.601246 +PAK,0.0,7.672781,166.691833 +PAN,0.0,0.0,0.335598 +PER,0.0,0.0,0.808195 +PHL,1.608594,0.297524,0.0 PRI,0.0,0.0,0.093587 -PRY,0.0,0.0,0.157387 -PSE,0.0,0.0,0.253417 -RWA,0.0,0.0,0.379823 -SAU,0.0,0.0,3.206031 -SDN,0.0,0.0,11.694079 -SEN,0.0,0.0,0.880737 -SOM,0.0,0.0,5.319843 -SSD,0.0,0.0,3.617177 +PRY,0.0,0.0,0.130072 +PSE,0.0,0.0,0.253437 +RWA,0.0,0.0,0.379596 +SAU,0.0,0.0,3.205631 +SDN,0.0,0.0,11.691299 +SEN,0.0,0.0,0.879997 +SOM,0.0,0.0,5.321395 +SSD,0.0,0.0,3.618346 STP,0.0,0.0,0.002251 -SWZ,0.0,0.0,0.169087 -SYR,0.0,0.0,1.271971 -TCD,0.0,0.0,25.861656 -THA,3.055352,0.0,0.0 -TJK,0.0,0.0,0.247159 -TKM,0.006912,0.0,0.695733 -TLS,0.012373,0.01037,0.066253 +SWZ,0.0,0.0,0.169131 +SYR,0.0,0.0,1.266636 +TCD,0.0,0.0,25.904709 +THA,1.262668,0.336465,0.0 +TJK,0.0,0.384083,0.245753 +TKM,0.0,0.0,0.692459 +TLS,0.010043,0.01037,0.067068 TTO,0.0,0.0,0.00687 -TUN,0.0,0.0,1.242338 -TUR,0.0,0.0,1.668762 -TWN,1.291355,0.00074,0.0 -TZA,0.0,0.0,9.636807 -UGA,0.0,0.0,3.555149 -URY,0.0,0.0,2.438578 -UZB,0.0,1.60441,0.0 -VEN,0.0,0.0,3.763246 -VNM,3.840014,0.712775,0.0 -YEM,0.0,0.0,3.183534 -ZAF,0.0,0.0,5.482439 -ZMB,0.0,0.0,2.941376 -ZWE,0.0,0.0,10.748847 +TUN,0.0,0.0,1.232322 +TUR,0.0,0.0,1.617232 +TWN,1.287958,0.0,0.0 +TZA,0.0,0.0,9.70183 +UGA,0.0,0.0,3.548997 +URY,0.0,0.0,2.438507 +VEN,0.0,0.0,3.760523 +YEM,0.0,0.0,3.182977 +ZAF,0.0,0.0,5.453259 +ZMB,0.0,0.0,2.947526 +ZWE,0.0,0.0,10.745438 diff --git a/data/curated/calibration/default/exogenous_forage.csv b/data/curated/calibration/default/exogenous_forage.csv index e3ba7281..f2b39ac9 100644 --- a/data/curated/calibration/default/exogenous_forage.csv +++ b/data/curated/calibration/default/exogenous_forage.csv @@ -5,7 +5,7 @@ country,exogenous_forage_mt_dm AFG,0.0 AGO,0.0 ALB,0.0 -ARE,0.290386 +ARE,1.955591 ARG,0.0 ARM,0.0 ATG,0.0 @@ -16,7 +16,7 @@ BDI,0.0 BEL,0.0 BEN,0.0 BFA,0.0 -BGD,17.304365 +BGD,16.47756 BGR,0.0 BHS,0.0 BIH,0.0 @@ -25,7 +25,7 @@ BLZ,0.0 BOL,0.0 BRA,0.0 BRB,0.0 -BRN,0.0 +BRN,0.073643 BTN,0.0 BWA,0.0 CAF,0.0 @@ -45,7 +45,7 @@ CUB,0.0 CYP,0.0 CZE,0.0 DEU,0.0 -DJI,0.0 +DJI,0.465381 DNK,0.0 DOM,0.0 DZA,0.0 @@ -76,7 +76,7 @@ HRV,0.0 HTI,0.0 HUN,0.0 IDN,0.0 -IND,133.659561 +IND,141.517197 IRL,0.0 IRN,0.0 IRQ,0.0 @@ -90,7 +90,7 @@ KAZ,0.0 KEN,0.0 KGZ,0.0 KHM,0.0 -KOR,5.239663 +KOR,0.0 LAO,0.0 LBN,0.0 LBR,0.0 @@ -107,7 +107,7 @@ MEX,0.0 MKD,0.0 MLI,0.0 MLT,0.0 -MMR,8.468348 +MMR,5.680842 MNE,0.0 MNG,0.0 MOZ,0.0 @@ -121,10 +121,10 @@ NGA,0.0 NIC,0.0 NLD,0.0 NOR,0.0 -NPL,2.597535 +NPL,2.535496 NZL,0.0 -OMN,1.69909 -PAK,70.964592 +OMN,1.702738 +PAK,71.103645 PAN,0.0 PER,0.0 PHL,0.0 @@ -156,19 +156,19 @@ SYR,0.0 TCD,0.0 TGO,0.0 THA,0.0 -TJK,3.292836 -TKM,9.849627 +TJK,3.656953 +TKM,0.0 TLS,0.0 TTO,0.0 TUN,0.0 -TUR,0.0 +TUR,15.814508 TWN,0.0 TZA,0.0 UGA,0.0 UKR,0.0 URY,0.0 USA,0.0 -UZB,15.041609 +UZB,12.951904 VEN,0.0 VNM,0.0 VUT,0.0 diff --git a/data/curated/calibration/default/fodder_conversion.csv b/data/curated/calibration/default/fodder_conversion.csv index 1fd05b76..f2f3ce92 100644 --- a/data/curated/calibration/default/fodder_conversion.csv +++ b/data/curated/calibration/default/fodder_conversion.csv @@ -53,7 +53,7 @@ ECU,1.0 EGY,1.0 ERI,1.0 ESP,1.0 -EST,0.475502 +EST,0.475451 ETH,1.0 FIN,1.0 FJI,1.0 @@ -74,7 +74,7 @@ GUY,1.0 HND,1.0 HRV,1.0 HTI,1.0 -HUN,0.592926 +HUN,0.594011 IDN,1.0 IND,1.0 IRL,1.0 @@ -85,7 +85,7 @@ ISR,1.0 ITA,1.0 JAM,1.0 JOR,1.0 -JPN,0.763731 +JPN,0.768006 KAZ,1.0 KEN,1.0 KGZ,1.0 @@ -99,9 +99,9 @@ LKA,1.0 LSO,1.0 LTU,1.0 LUX,1.0 -LVA,0.294322 +LVA,0.29539 MAR,1.0 -MDA,0.857278 +MDA,0.853737 MDG,1.0 MEX,1.0 MKD,1.0 @@ -128,13 +128,13 @@ PAK,1.0 PAN,1.0 PER,1.0 PHL,1.0 -PNG,0.986016 +PNG,0.98085 POL,1.0 PRI,1.0 PRT,1.0 PRY,1.0 PSE,1.0 -ROU,0.753901 +ROU,0.754488 RUS,1.0 RWA,1.0 SAU,1.0 @@ -148,7 +148,7 @@ SRB,1.0 SSD,1.0 STP,1.0 SUR,1.0 -SVK,0.954113 +SVK,0.953837 SVN,1.0 SWE,1.0 SWZ,1.0 diff --git a/data/curated/calibration/default/food_demand.csv b/data/curated/calibration/default/food_demand.csv index 4ccd01de..27f57e44 100644 --- a/data/curated/calibration/default/food_demand.csv +++ b/data/curated/calibration/default/food_demand.csv @@ -1,60 +1,60 @@ food,multiplier,net_slack_mt,consumption_mt -apple,1.0266644745409084,-2.6771347868198063,100.40080792563822 -banana,1.0,0.0,64.1017096311407 +apple,1.0277016578888032,-2.7832508087158203,100.47235511636245 +banana,1.0,0.0,64.1473880019621 barley-hulled,1.0,0.0,2.5509975362103887 -buckwheat,0.5297444815536061,2.452242771270221,5.21470280534254 -cabbage,0.99410357957926,1.3808388083562022,234.1825565048348 -carrot,1.0009741534656378,-0.12149969721212983,124.72336392354919 -cassava,1.0,0.0,156.529094039166 -chickpea,0.7380542588377743,3.1101202494776317,11.873146842083997 -citrus,1.071883770937358,-9.327037811279297,129.751648941834 -cocoa-powder,0.9075724764640558,0.4894329881935846,5.295316475760046 +buckwheat,0.5295157808376175,2.4534353775354703,5.21470280534254 +cabbage,0.9928474519825207,1.6707932747667655,233.59413605944428 +carrot,1.0016805383393474,-0.20907572656869888,124.40997130115284 +cassava,1.0,0.0,156.39024045774022 +chickpea,0.7382112624152889,3.1082561229470684,11.873146842083997 +citrus,1.0719887702981454,-9.347317576408386,129.84410676409607 +cocoa-powder,0.902422148467906,0.5167018563806778,5.2952780602136045 coconut,1.0,0.0,7.4685584381240915 coconut-oil,1.0,0.0,0.8683887740007776 -coffee-green,0.8670346719587236,1.5567073597339913,11.707618690270465 +coffee-green,0.8670342455798214,1.5567072983831167,11.707580686255824 cottonseed-oil,1.0,0.0,3.125593973028117 cowpea,1.0,0.0,8.023896933055994 -dairy,1.1191858501944325,-83.19951275270432,698.0653543770313 -dairy-buffalo,1.1373173584109617,-16.630459283856908,121.1096650584077 +dairy,1.1191858492658655,-83.19951210450381,698.0653543770313 +dairy-buffalo,1.1373173584108416,-16.630459283842356,121.1096650584077 dry-pea,1.0,0.0,6.17437933254223 -eggs,0.9999999933922069,5.425278004622669e-07,82.1042355971731 +eggs,1.0,0.0,82.1042355971731 flour-white,1.0,0.0,275.2358510554768 flour-wholemeal,1.0,0.0,38.87509628526459 foxtail-millet,1.0,0.0,3.234954914529226 -gram,0.7380542919392032,3.110119856459505,11.873146842083997 +gram,0.7382112626506071,3.1082561201531007,11.873146842083997 groundnut,1.0,0.0,20.996472433620966 groundnut-oil,1.0,0.0,2.783429691281526 maize,1.0,0.0,64.77057215915829 maize-whole,1.0,0.0,20.808036830817628 -mango,0.8347028290816869,9.925095592123398,60.04395318434217 -meat-cattle,0.952400557178112,2.308293342590332,48.49412526166998 -meat-chicken,0.9895527158773688,0.8225271608680487,78.7311947500566 -meat-pig,1.0184832063942477,-1.3977279365062714,75.62150779971125 -meat-sheep,1.0872487686362589,-0.8913660735124722,10.21637425312656 +mango,0.8336838331135494,9.993395872465044,60.08673756465214 +meat-cattle,0.9524005589809676,2.3082932551624253,48.49412526166998 +meat-chicken,0.9895527156644442,0.822527177631855,78.7311947500566 +meat-pig,1.0184832060001492,-1.397727906703949,75.62150779971125 +meat-sheep,1.0872487675309466,-0.891366062220186,10.21637425312656 oat,1.0,0.0,1.1316055530789981 -olive-oil,1.2983651782181878,-1.1316246474161744,3.7927503945807075 -onion,0.9920661276212404,2.2518915306172858,283.8325880620396 -palm-oil,0.5,36.719671779326745,21.10606127652136 +olive-oil,1.299325925599697,-1.135268522426486,3.7927503945807075 +onion,0.9917853867045111,2.3257164411770646,283.11940654032514 +palm-oil,0.5,36.668308315522154,21.10606127652136 pearl-millet,1.0,0.0,3.234954914529226 phaseolus-bean,1.0,0.0,21.57145358827256 pigeon-pea,1.0,0.0,0.6679289132182475 -plantain,0.8963608878878673,2.6173826385456778,25.25477674600097 -potato,1.0,0.0,225.42070956376847 +plantain,0.8971522134303425,2.595093748099316,25.23237334176065 +potato,1.0,0.0,225.22074107383378 rapeseed-oil,1.0,0.0,10.001032957413827 rendered-fat,1.0,0.0,7.44342613070603 rice-brown,1.0,0.0,35.70415660064464 rice-white,1.0,0.0,340.82733609573916 rye,1.0,0.0,0.809679446935661 -sesame-oil,0.9676143890208987,0.032453327785333386,1.0020909534878228 +sesame-oil,0.973256769509102,0.026799149341968587,1.0020909534878228 sesame-seed,1.0,0.0,2.9894916711275528 sorghum,1.0,0.0,11.638505917195289 soy,1.0,0.0,11.041815498637334 soybean-oil,1.0,0.0,25.179107923099764 sugar,1.0,0.0,137.2949263714254 -sunflower-oil,0.9999382360909597,0.0007390549872070551,11.965806547706507 +sunflower-oil,1.0,0.0,11.965806547706507 sunflower-seed,1.0,0.0,4.64019518993814 -sweet-potato,1.0,0.0,44.74315627075703 -tea-dried,0.9839852983705806,0.10687886715913919,6.673796966832015 -tomato,1.027724820712193,-3.511240005493164,126.6460851791544 -watermelon,1.1469571407924075,-8.598299026489258,58.50888891908471 -yam,0.9337672544516643,2.296284371904221,34.66992577302153 +sweet-potato,1.0,0.0,44.70346673552558 +tea-dried,0.9840023658971014,0.10676424254961603,6.673751997507679 +tomato,1.0299810788704098,-3.7874455749988556,126.32786136115101 +watermelon,1.1466405266452742,-8.585887908935547,58.550580152408656 +yam,0.9213296051922765,2.7274955768052678,34.669910879073086 diff --git a/data/curated/calibration/default/food_waste.yaml b/data/curated/calibration/default/food_waste.yaml index 41d0f8e5..3b891020 100644 --- a/data/curated/calibration/default/food_waste.yaml +++ b/data/curated/calibration/default/food_waste.yaml @@ -10,26 +10,26 @@ # `tools/calibrate food_waste` instead. fruits: baseline_consumption_mt: 344.04602138686823 - baseline_negative_slack_mt: 68.76098508550785 + baseline_negative_slack_mt: 69.0551507700875 baseline_positive_slack_mt: 0.0 - waste_retention_multiplier: 0.8334306733960215 + waste_retention_multiplier: 0.8328371947977665 oil: baseline_consumption_mt: 88.23545169174031 - baseline_negative_slack_mt: 0.7936579056549817 - baseline_positive_slack_mt: 42.464086364731884 - waste_retention_multiplier: 1.8948868821748348 + baseline_negative_slack_mt: 0.7973017692565918 + baseline_positive_slack_mt: 42.15683766342431 + waste_retention_multiplier: 1.8823195278524985 starchy_vegetable: baseline_consumption_mt: 491.43520556430417 baseline_negative_slack_mt: 0.0 - baseline_positive_slack_mt: 5.183033215001387 - waste_retention_multiplier: 1.0106591466521575 + baseline_positive_slack_mt: 5.614373094470693 + waste_retention_multiplier: 1.0115564683917075 stimulants: baseline_consumption_mt: 23.68795094791676 baseline_negative_slack_mt: 0.0 - baseline_positive_slack_mt: 2.1642380495459292 - waste_retention_multiplier: 1.1005513342314535 + baseline_positive_slack_mt: 2.191513587999907 + waste_retention_multiplier: 1.101947757728743 vegetables: baseline_consumption_mt: 717.5079036276238 - baseline_negative_slack_mt: 51.876708737108856 + baseline_negative_slack_mt: 49.94347468810156 baseline_positive_slack_mt: 0.0 - waste_retention_multiplier: 0.9325737636243285 + waste_retention_multiplier: 0.9349229461315072 diff --git a/data/curated/calibration/default/grassland_cost.csv b/data/curated/calibration/default/grassland_cost.csv index 92c918bf..23062ba3 100644 --- a/data/curated/calibration/default/grassland_cost.csv +++ b/data/curated/calibration/default/grassland_cost.csv @@ -1,175 +1,175 @@ country,correction_bnusd_per_mha -AFG,1.0285317042502129e-10 -AGO,-0.3685210794210434 -ALB,0.04497191123664379 -ARE,0.0 -ARG,-0.149925597012043 -ARM,0.0 -ATG,0.0 -AUS,-0.00915704108774662 -AUT,0.0 -AZE,0.9814981520175934 -BDI,-0.48811182379722595 -BEL,0.08367127552628517 -BEN,3.2785658836364746 -BFA,1.5074808597564697 -BGD,0.0 -BGR,-0.22096917033195496 -BHS,4.3433148744220773e-10 -BIH,-0.42985886335372925 -BLR,0.011688394472002983 -BLZ,-0.8535727262496948 -BOL,-0.5958932638168335 -BRA,-0.3751921057701111 -BRB,0.9317607879638672 -BRN,7.141561985015869 -BTN,-0.30002422630786896 -BWA,0.0 -CAF,3.2490773627102953e-08 -CAN,-0.21738236397504807 -CHE,0.0 -CHL,-0.0009876639232970774 -CHN,-9.942733303347495e-10 -CIV,-0.503073513507843 -CMR,4.431897163391113 -COD,-0.7558623552322388 -COG,-0.8903338015079498 -COL,-0.7592038810253143 -COM,1.4762386679649353 -CPV,0.0 -CRI,-0.09060221165418625 -CUB,-0.1725609302520752 -CYP,0.30934228003025055 -CZE,-0.006804863223806024 -DEU,-0.2437824308872223 -DJI,0.0 -DNK,1.8209227323532104 -DOM,-1.4828121619459012e-08 -DZA,1.8697272896606165e-10 -ECU,-0.10834560729563236 -EGY,0.0 -ERI,-0.036436695605516434 -ESP,4.5370475432182644e-10 -EST,-0.25669121742248535 -ETH,1.5634718408819026e-08 -FIN,1.712095022201538 -FJI,-0.7755072712898254 -FRA,-2.7449107875554546e-08 -GAB,-0.8109605014324188 -GBR,0.0 -GEO,-0.2034044936299324 -GHA,-0.5135024040937424 -GIN,-0.15879005938768387 -GMB,1.2291799783706665 -GNB,1.1270533204078674 -GNQ,-0.7098988890647888 -GRC,-0.09379514306783676 -GRD,0.6720268130302429 -GTM,5.198906860570673e-10 -GUF,-1.0722496509552002 -GUY,-1.106971263885498 -HND,-0.5874203741550446 -HRV,0.0 -HTI,-6.526291063124745e-09 -HUN,-0.4170793741941452 -IDN,-0.5079740881919861 -IND,1.584054708480835 -IRL,0.0 -IRN,0.0 -IRQ,0.0 -ISL,-0.06565051525831223 -ISR,0.7160363793373108 -ITA,0.29519903659820557 -JAM,3.780540943145752 -JOR,0.0 -JPN,-0.4974181354045868 -KAZ,0.0 -KEN,-0.12902680039405823 -KGZ,-1.187652864942379e-09 -KHM,-0.27926672995090485 -KOR,3.1221169233322144 -LAO,-3.513972668400811e-09 -LBN,0.5231764614582062 -LBR,-0.7535805702209473 -LBY,0.0 -LKA,-4.2231160790606737e-08 -LSO,-0.4797108322381973 -LTU,-1.0227096947090786e-10 -LUX,0.061516094487160444 -LVA,-0.3037813901901245 -MAR,0.0 -MDA,-0.36603914201259613 -MDG,-0.6528348624706268 -MEX,6.248633832584005e-09 -MKD,-0.1847551316022873 -MLI,-0.04440104402601719 -MLT,0.0 -MMR,0.7816084325313568 -MNE,-0.157601498067379 -MNG,-0.02809592429548502 -MOZ,-0.5656761825084686 -MRT,-2.2212138378607804e-11 -MUS,2.5759505639655345e-09 -MWI,-0.19057554006576538 -MYS,-9.89881137059001e-08 -NAM,-0.006840692600235343 -NER,0.0 -NGA,-0.19652122259140015 -NIC,-0.5845303535461426 -NLD,0.7907610237598419 -NOR,0.583920419216156 -NPL,0.0 -NZL,0.0 -OMN,0.0 -PAK,1.424497075852571e-09 -PAN,-0.10266764834523201 -PER,-0.6257849335670471 -PHL,-0.12064796686172485 -PNG,-0.7848113179206848 -POL,0.019568821415305138 -PRI,-6.72518289945856e-08 -PRT,-0.09565510042011738 -PRY,-0.5369450151920319 -PSE,0.7072950899600983 -ROU,-0.4245498776435852 -RUS,-1.0847077902553792e-08 -RWA,6.476653575897217 -SAU,0.0 -SDN,-0.023131180554628372 -SEN,0.3793909251689911 -SLB,1.598713561534737e-08 -SLE,-0.4092519134283066 -SLV,3.6254513263702393 -SOM,-4.7665072999691915e-09 -SRB,-0.295737162232399 -SSD,-0.2969408631324768 -STP,0.6137635111808777 -SUR,6.86613392829895 -SVK,-0.4092821776866913 -SVN,1.956440476291732e-08 -SWE,1.7676981091499329 -SWZ,-1.973749874650821e-08 -SYR,0.14269284904003143 -TCD,-0.07812828943133354 -TGO,-0.46927402913570404 -THA,0.0 -TJK,0.3895326554775238 -TKM,-9.089148877983177e-10 -TLS,-0.3032049834728241 -TTO,8.520019217916897e-09 -TUN,0.3667435646057129 -TUR,1.1556978225708008 -TWN,-0.1748160943388939 -TZA,-0.06839879974722862 -UGA,-0.6480332314968109 -UKR,0.06035628356039524 -URY,-0.21174990385770798 -USA,-0.4049943536520004 -UZB,1.0099180340766907 -VEN,-0.26628775894641876 -VNM,5.704484701156616 -VUT,-0.13240307196974754 -YEM,0.0 -ZAF,0.0 -ZMB,-0.15246352553367615 -ZWE,1.0522664785385132 +AFG,-4.0427408296017653e-10 +AGO,-0.38177555799484253 +ALB,-0.17037621140480042 +ARE,-0.0 +ARG,-0.18566030263900757 +ARM,-0.0 +ATG,-0.0 +AUS,-0.030936773866415024 +AUT,9.24111176470177e-10 +AZE,0.8003178536891937 +BDI,-0.49132993817329407 +BEL,0.08877222612500191 +BEN,2.4552853107452393 +BFA,1.1361154317855835 +BGD,4.882810354232788 +BGR,-0.2715888172388077 +BHS,-0.03126266412436962 +BIH,-0.44524990022182465 +BLR,-8.282973240625324e-08 +BLZ,-0.7399452030658722 +BOL,-0.6221835613250732 +BRA,-0.5207413733005524 +BRB,0.7491881251335144 +BRN,-3.437714735099462e-08 +BTN,-0.3210534453392029 +BWA,-0.0 +CAF,-0.44557350873947144 +CAN,-0.21650707721710205 +CHE,-0.0 +CHL,-0.03965021949261427 +CHN,-6.799676377511332e-09 +CIV,-0.5334059000015259 +CMR,2.3226504921913147 +COD,-0.7531084418296814 +COG,-0.8099571466445923 +COL,-0.6204277873039246 +COM,1.216862440109253 +CPV,-0.0 +CRI,-0.04483989253640175 +CUB,-0.17085150629281998 +CYP,0.2554362267255783 +CZE,-0.12467751279473305 +DEU,-0.24095454812049866 +DJI,-0.0 +DNK,1.6213496327400208 +DOM,-0.4018055945634842 +DZA,-2.098951901174928e-09 +ECU,-0.19687405228614807 +EGY,-0.0 +ERI,-0.04815981723368168 +ESP,-0.020102565176784992 +EST,-0.26680906116962433 +ETH,-1.9732169675990008e-08 +FIN,1.3795255422592163 +FJI,-0.8463798761367798 +FRA,1.5480199344608536e-08 +GAB,-0.8217355906963348 +GBR,-8.699288400748628e-09 +GEO,-0.20096751302480698 +GHA,-0.5431598126888275 +GIN,-0.37204913794994354 +GMB,1.003356397151947 +GNB,0.9321905374526978 +GNQ,-0.7101413011550903 +GRC,-0.150973878800869 +GRD,0.5272164940834045 +GTM,-2.610952076764761e-08 +GUF,-0.9834447503089905 +GUY,-1.1032667756080627 +HND,-0.6275273263454437 +HRV,-0.13127634674310684 +HTI,1.2459450360324809e-08 +HUN,-0.4085644632577896 +IDN,-0.562181144952774 +IND,1.3695911765098572 +IRL,-0.0 +IRN,-0.0 +IRQ,-0.0 +ISL,-0.07798495143651962 +ISR,0.5899375379085541 +ITA,0.42469048500061035 +JAM,3.039083242416382 +JOR,-0.0 +JPN,-0.4728243798017502 +KAZ,-1.748426403658243e-09 +KEN,-0.2179667055606842 +KGZ,-7.723922612612455e-10 +KHM,-0.3935643285512924 +KOR,2.681120753288269 +LAO,-0.2158590704202652 +LBN,0.4323943108320236 +LBR,-0.7618840336799622 +LBY,2.1369450653452304e-10 +LKA,-0.39817817509174347 +LSO,-0.48218031227588654 +LTU,-0.0 +LUX,0.01513665821403265 +LVA,-0.2936539202928543 +MAR,-0.0 +MDA,-0.3453979641199112 +MDG,-0.6868391931056976 +MEX,-0.0679156482219696 +MKD,-0.25515754520893097 +MLI,-0.037232923321425915 +MLT,-0.0 +MMR,2.3120899200439453 +MNE,-0.21985548734664917 +MNG,-0.029697048477828503 +MOZ,-0.5160333812236786 +MRT,-0.033999696373939514 +MUS,-0.07080470398068428 +MWI,-3.3964567824895653e-09 +MYS,-0.2939038872718811 +NAM,-0.0047507211565971375 +NER,-0.0 +NGA,-0.14038769900798798 +NIC,-0.5779949724674225 +NLD,0.8321638405323029 +NOR,1.1431958079338074 +NPL,0.12020483240485191 +NZL,-0.0 +OMN,-0.0 +PAK,-0.0 +PAN,-0.6037594079971313 +PER,-0.6955994665622711 +PHL,-0.11315194144845009 +PNG,-0.8056034743785858 +POL,0.005302567966282368 +PRI,-0.6564330756664276 +PRT,-0.15870346128940582 +PRY,-0.6874429285526276 +PSE,0.5565134882926941 +ROU,-0.4107300937175751 +RUS,-2.4574029477975046e-09 +RWA,1.4384679225543096e-08 +SAU,-0.0 +SDN,-0.051845161244273186 +SEN,0.31076647341251373 +SLB,-1.6454283713329687e-08 +SLE,-0.3628302663564682 +SLV,2.9411346912384033 +SOM,-0.05690718814730644 +SRB,-0.2870781123638153 +SSD,-0.2865627706050873 +STP,0.49346114695072174 +SUR,-0.13022971898317337 +SVK,-0.4101209193468094 +SVN,-0.10804693028330803 +SWE,1.2120238542556763 +SWZ,-2.0683636137164285e-08 +SYR,0.1182437390089035 +TCD,-0.08657535165548325 +TGO,-0.30542606115341187 +THA,-1.830725793183774e-08 +TJK,0.3392861485481262 +TKM,-0.0 +TLS,-0.22256753593683243 +TTO,-3.4331349985095017e-09 +TUN,0.7436896860599518 +TUR,0.8467051684856415 +TWN,-0.3354679197072983 +TZA,-0.40795499086380005 +UGA,-3.941181603295263e-08 +UKR,-0.007475577760487795 +URY,-0.38374826312065125 +USA,-0.2134283185005188 +UZB,-0.0 +VEN,-0.340562105178833 +VNM,4.7374420166015625 +VUT,-0.19533131271600723 +YEM,2.167515278372889e-09 +ZAF,-0.04584670439362526 +ZMB,-0.39802390336990356 +ZWE,0.4092240985482931 diff --git a/data/curated/calibration/default/grassland_yield.csv b/data/curated/calibration/default/grassland_yield.csv index ab4cfcdd..6adf723d 100644 --- a/data/curated/calibration/default/grassland_yield.csv +++ b/data/curated/calibration/default/grassland_yield.csv @@ -2,177 +2,177 @@ # # SPDX-License-Identifier: CC-BY-4.0 country,yield_correction -AFG,0.395294 -AGO,0.071585 -ALB,0.144938 +AFG,0.3953 +AGO,0.071477 +ALB,0.145104 ARE,1.0 -ARG,0.345713 -ARM,0.550552 +ARG,0.200708 +ARM,0.550888 ATG,1.0 -AUS,0.236373 -AUT,0.600427 +AUS,0.249267 +AUT,0.589604 AZE,1.0 -BDI,0.442052 -BEL,0.928966 +BDI,0.437994 +BEL,0.932016 BEN,1.0 BFA,1.0 BGD,1.0 -BGR,0.074007 -BHS,0.669108 -BIH,0.01467 -BLR,0.422193 -BLZ,0.102024 -BOL,0.111315 -BRA,0.229034 +BGR,0.072907 +BHS,0.669107 +BIH,0.014664 +BLR,0.420747 +BLZ,0.101966 +BOL,0.11001 +BRA,0.23009 BRB,1.0 BRN,1.0 -BTN,0.03586 +BTN,0.035842 BWA,1.0 -CAF,0.583977 -CAN,0.051832 -CHE,0.40712 -CHL,0.686938 -CHN,0.290638 -CIV,0.033413 +CAF,0.598793 +CAN,0.048283 +CHE,0.409804 +CHL,0.711568 +CHN,0.287527 +CIV,0.032799 CMR,1.0 -COD,0.022128 -COG,0.00797 -COL,0.103539 +COD,0.022132 +COG,0.00757 +COL,0.104125 COM,1.0 CPV,1.0 -CRI,0.380109 -CUB,0.277685 +CRI,0.376606 +CUB,0.276615 CYP,1.0 -CZE,0.232729 -DEU,0.150859 +CZE,0.232716 +DEU,0.156978 DJI,1.0 DNK,1.0 -DOM,0.567981 -DZA,0.791718 -ECU,0.36376 +DOM,0.550096 +DZA,0.71264 +ECU,0.354057 EGY,1.0 -ERI,0.518486 -ESP,0.429751 +ERI,0.518822 +ESP,0.428108 EST,0.0 -ETH,0.551195 +ETH,0.551758 FIN,1.0 -FJI,0.041419 -FRA,0.488102 -GAB,0.007952 -GBR,0.40073 -GEO,0.122798 -GHA,0.178956 -GIN,0.173001 +FJI,0.040158 +FRA,0.490452 +GAB,0.005941 +GBR,0.400867 +GEO,0.121039 +GHA,0.16792 +GIN,0.173016 GMB,1.0 GNB,1.0 -GNQ,0.077495 -GRC,0.075784 +GNQ,0.077511 +GRC,0.073372 GRD,1.0 -GTM,0.571477 +GTM,0.584133 GUF,0.0 -GUY,0.018762 -HND,0.186551 -HRV,0.284196 -HTI,0.92669 +GUY,0.01813 +HND,0.148916 +HRV,0.283024 +HTI,0.925315 HUN,0.0 -IDN,0.144268 +IDN,0.144689 IND,1.0 -IRL,0.633733 -IRN,0.769453 +IRL,0.635826 +IRN,0.806529 IRQ,1.0 -ISL,0.062237 +ISL,0.063932 ISR,1.0 ITA,1.0 JAM,1.0 JOR,1.0 JPN,0.0 -KAZ,0.256835 -KEN,0.467676 -KGZ,0.842055 -KHM,0.398086 +KAZ,0.257373 +KEN,0.467622 +KGZ,0.841994 +KHM,0.398064 KOR,1.0 -LAO,0.446981 +LAO,0.400714 LBN,1.0 -LBR,0.016113 -LBY,0.894499 -LKA,0.193473 -LSO,0.043373 -LTU,0.341665 -LUX,0.783687 +LBR,0.01603 +LBY,0.883798 +LKA,0.192633 +LSO,0.048298 +LTU,0.340422 +LUX,0.787131 LVA,0.0 MAR,1.0 MDA,0.0 -MDG,0.016111 -MEX,0.371319 -MKD,0.104967 -MLI,0.387873 +MDG,0.016071 +MEX,0.379061 +MKD,0.104998 +MLI,0.392024 MLT,1.0 MMR,1.0 -MNE,0.077905 -MNG,0.245052 -MOZ,0.00289 -MRT,0.66052 +MNE,0.077909 +MNG,0.245828 +MOZ,0.002894 +MRT,0.662616 MUS,0.811415 -MWI,0.76798 -MYS,0.442334 -NAM,0.29193 +MWI,0.759081 +MYS,0.436504 +NAM,0.327938 NER,1.0 -NGA,0.665316 -NIC,0.295306 +NGA,0.680682 +NIC,0.299317 NLD,1.0 NOR,1.0 NPL,1.0 -NZL,0.560406 +NZL,0.558077 OMN,1.0 PAK,1.0 -PAN,0.312528 -PER,0.084126 -PHL,0.384506 +PAN,0.254535 +PER,0.09729 +PHL,0.380175 PNG,0.0 -POL,0.560559 -PRI,0.459587 -PRT,0.081039 -PRY,0.068489 +POL,0.560699 +PRI,0.43587 +PRT,0.112791 +PRY,0.069461 PSE,1.0 ROU,0.0 -RUS,0.15561 -RWA,1.0 +RUS,0.154765 +RWA,0.794253 SAU,1.0 -SDN,0.419271 +SDN,0.418012 SEN,1.0 -SLB,0.408618 -SLE,0.093191 +SLB,0.575706 +SLE,0.091711 SLV,1.0 -SOM,0.555429 -SRB,0.136713 -SSD,0.142093 +SOM,0.542251 +SRB,0.141601 +SSD,0.141785 STP,1.0 -SUR,1.0 +SUR,0.814601 SVK,0.0 -SVN,0.252012 +SVN,0.252093 SWE,1.0 -SWZ,0.283263 +SWZ,0.283244 SYR,1.0 -TCD,0.695865 -TGO,0.206827 -THA,0.898892 +TCD,0.697183 +TGO,0.199885 +THA,0.860523 TJK,1.0 TKM,1.0 -TLS,0.234166 -TTO,0.839948 +TLS,0.233632 +TTO,0.743021 TUN,1.0 TUR,1.0 -TWN,0.107613 -TZA,0.407102 -UGA,0.425128 -UKR,0.166781 -URY,0.242289 -USA,0.031197 +TWN,0.108669 +TZA,0.405365 +UGA,0.394319 +UKR,0.167267 +URY,0.270061 +USA,0.03083 UZB,1.0 -VEN,0.190094 +VEN,0.188842 VNM,1.0 -VUT,0.176534 -YEM,0.592561 -ZAF,0.346808 -ZMB,0.203235 +VUT,0.163781 +YEM,0.586928 +ZAF,0.340447 +ZMB,0.202565 ZWE,1.0 diff --git a/data/curated/calibration/default/multi_crop_cost.csv b/data/curated/calibration/default/multi_crop_cost.csv index 5217f5cc..c762260e 100644 --- a/data/curated/calibration/default/multi_crop_cost.csv +++ b/data/curated/calibration/default/multi_crop_cost.csv @@ -1,101 +1,102 @@ combination,country,correction_bnusd_per_mha -cotton_wheat,AFG,0.12073299288749695 -cotton_wheat,AGO,1.2058953940868378 +cotton_wheat,AFG,-1.5927236080169678 +cotton_wheat,AGO,-1.9337581396102905 cotton_wheat,ALB,7.5 cotton_wheat,ARG,7.5 cotton_wheat,ARM,7.5 -cotton_wheat,AUS,-7.5 +cotton_wheat,AUS,-6.242731809616089 cotton_wheat,AZE,7.5 -cotton_wheat,BDI,-0.5172154903411865 -cotton_wheat,BGD,4.8990325927734375 +cotton_wheat,BDI,-0.9871918559074402 +cotton_wheat,BGD,-1.534177005290985 cotton_wheat,BGR,7.5 cotton_wheat,BOL,7.5 -cotton_wheat,BRA,6.710078716278076 -cotton_wheat,BWA,4.441415369510651 -cotton_wheat,CHN,1.1753121614456177 -cotton_wheat,COD,-1.0172451734542847 +cotton_wheat,BRA,-2.6939632892608643 +cotton_wheat,BWA,-1.3231643438339233 +cotton_wheat,CHN,-6.612751007080078 +cotton_wheat,COD,-1.9223291873931885 cotton_wheat,DZA,7.5 -cotton_wheat,EGY,0.7446867227554321 -cotton_wheat,ESP,2.422280430793762 -cotton_wheat,ETH,-1.18629789352417 +cotton_wheat,EGY,-2.767568826675415 +cotton_wheat,ESP,-1.7561184763908386 +cotton_wheat,ETH,-2.117214798927307 cotton_wheat,GEO,7.5 -cotton_wheat,GRC,1.8530462980270386 -cotton_wheat,HND,-0.027787355706095695 -cotton_wheat,IND,-0.6717728674411774 -cotton_wheat,IRN,0.558936595916748 -cotton_wheat,IRQ,7.5 -cotton_wheat,ISR,2.486711263656616 -cotton_wheat,JOR,2.592496156692505 -cotton_wheat,KAZ,1.498054027557373 -cotton_wheat,KEN,1.0787805318832397 -cotton_wheat,KGZ,2.308807134628296 +cotton_wheat,GRC,-2.2709689140319824 +cotton_wheat,HND,-1.9976805448532104 +cotton_wheat,IND,-1.5435030460357666 +cotton_wheat,IRN,-2.3831645250320435 +cotton_wheat,IRQ,-1.8188846111297607 +cotton_wheat,ISR,-0.583361029624939 +cotton_wheat,JOR,-2.195621132850647 +cotton_wheat,KAZ,-2.069269299507141 +cotton_wheat,KEN,-0.7512478828430176 +cotton_wheat,KGZ,-1.4307011365890503 cotton_wheat,KOR,7.5 cotton_wheat,LBN,7.5 -cotton_wheat,LSO,4.406145870685577 -cotton_wheat,MAR,1.0710368156433105 -cotton_wheat,MDG,-0.14180460572242737 -cotton_wheat,MEX,6.028358459472656 -cotton_wheat,MKD,3.2505438327789307 +cotton_wheat,LSO,3.307736039161682 +cotton_wheat,MAR,-2.9455323219299316 +cotton_wheat,MDG,-1.7016127109527588 +cotton_wheat,MEX,-3.390718936920166 +cotton_wheat,MKD,-1.731279969215393 cotton_wheat,MMR,7.5 -cotton_wheat,MOZ,0.47627148032188416 -cotton_wheat,MWI,7.5 -cotton_wheat,NAM,4.1794014275074005 -cotton_wheat,NER,7.5 -cotton_wheat,NGA,-7.5 +cotton_wheat,MOZ,-1.383345603942871 +cotton_wheat,MWI,2.7875921726226807 +cotton_wheat,NAM,-1.9929966926574707 +cotton_wheat,NER,-0.795464962720871 +cotton_wheat,NGA,-1.8842791318893433 cotton_wheat,NPL,7.5 -cotton_wheat,PAK,1.4043108224868774 +cotton_wheat,OMN,-0.0 +cotton_wheat,PAK,-1.3193728923797607 cotton_wheat,PRT,7.5 -cotton_wheat,PRY,2.0446081161499023 -cotton_wheat,PSE,1.3241363763809204 -cotton_wheat,ROU,0.0 +cotton_wheat,PRY,-0.7623048722743988 +cotton_wheat,PSE,-1.8203025460243225 +cotton_wheat,ROU,-0.0 cotton_wheat,RUS,7.5 -cotton_wheat,RWA,-0.284987673163414 -cotton_wheat,SDN,-7.5 -cotton_wheat,SOM,-7.5 -cotton_wheat,SWZ,-0.8469589948654175 -cotton_wheat,SYR,2.0918221473693848 -cotton_wheat,TJK,0.07361793518066406 -cotton_wheat,TKM,0.5607117414474487 +cotton_wheat,RWA,-1.5891630053520203 +cotton_wheat,SDN,-1.7169711589813232 +cotton_wheat,SOM,2.795064091682434 +cotton_wheat,SWZ,-1.7710586786270142 +cotton_wheat,SYR,-1.432285189628601 +cotton_wheat,TJK,-3.2881886959075928 +cotton_wheat,TKM,-1.6561976671218872 cotton_wheat,TUN,7.5 -cotton_wheat,TUR,4.218727111816406 -cotton_wheat,TZA,-0.6717642545700073 -cotton_wheat,UGA,-0.21029487997293472 +cotton_wheat,TUR,-0.9880382418632507 +cotton_wheat,TZA,-1.4977788925170898 +cotton_wheat,UGA,-1.763108491897583 cotton_wheat,URY,7.5 -cotton_wheat,USA,2.4452409744262695 -cotton_wheat,UZB,0.42546316981315613 -cotton_wheat,YEM,-7.5 -cotton_wheat,ZAF,2.9171504974365234 +cotton_wheat,USA,-1.4581074714660645 +cotton_wheat,UZB,-1.7531643509864807 +cotton_wheat,YEM,-2.3453104496002197 +cotton_wheat,ZAF,-2.8740453720092773 cotton_wheat,ZMB,7.5 -cotton_wheat,ZWE,6.102042198181152 +cotton_wheat,ZWE,3.7642102241516113 double_rice,AFG,7.5 double_rice,AGO,7.5 -double_rice,ALB,0.0 +double_rice,ALB,-0.0 double_rice,ARG,7.5 double_rice,AUS,7.5 double_rice,AZE,7.5 -double_rice,BDI,-6.651249885559082 -double_rice,BEN,-2.4684728384017944 -double_rice,BFA,-7.301760196685791 -double_rice,BGD,-0.13437831960618496 -double_rice,BLZ,2.286782741546631 +double_rice,BDI,3.46013343334198 +double_rice,BEN,-2.2166402339935303 +double_rice,BFA,-2.23768150806427 +double_rice,BGD,-0.7278321385383606 +double_rice,BLZ,2.154790997505188 double_rice,BOL,7.5 double_rice,BRA,7.5 -double_rice,BRN,-2.890824794769287 +double_rice,BRN,-2.9397683143615723 double_rice,BTN,7.5 -double_rice,BWA,0.0 +double_rice,BWA,-0.0 double_rice,CAF,7.5 double_rice,CHL,7.5 -double_rice,CHN,-3.0934839248657227 +double_rice,CHN,-3.6336244344711304 double_rice,CIV,7.5 -double_rice,CMR,7.5 +double_rice,CMR,-2.1459217071533203 double_rice,COD,7.5 double_rice,COG,7.5 -double_rice,COL,-2.408092975616455 -double_rice,CRI,-1.5242087841033936 +double_rice,COL,-2.7271934747695923 +double_rice,CRI,-1.8571616411209106 double_rice,CUB,7.5 -double_rice,DOM,-2.50664484500885 +double_rice,DOM,2.0781034231185913 double_rice,DZA,7.5 -double_rice,ECU,-1.0761107802391052 +double_rice,ECU,-1.2515701651573181 double_rice,EGY,7.5 double_rice,ERI,7.5 double_rice,ESP,7.5 @@ -104,390 +105,390 @@ double_rice,FJI,7.5 double_rice,FRA,7.5 double_rice,GAB,7.5 double_rice,GEO,7.5 -double_rice,GHA,-0.5125921368598938 -double_rice,GIN,-7.5 -double_rice,GMB,-7.5 -double_rice,GNB,-7.5 +double_rice,GHA,-0.8532624244689941 +double_rice,GIN,-1.1334679126739502 +double_rice,GMB,-0.05453077703714371 +double_rice,GNB,-2.5362197160720825 double_rice,GNQ,7.5 double_rice,GRC,7.5 -double_rice,GTM,5.432131886482239 -double_rice,GUF,0.0 +double_rice,GTM,2.2990827560424805 +double_rice,GUF,7.5 double_rice,GUY,7.5 double_rice,HND,7.5 -double_rice,HTI,2.532879590988159 -double_rice,IDN,-2.5864267349243164 -double_rice,IND,2.284341335296631 +double_rice,HTI,2.4523184299468994 +double_rice,IDN,-2.7687764167785645 +double_rice,IND,-1.3916572332382202 double_rice,IRN,7.5 double_rice,IRQ,7.5 double_rice,ITA,7.5 -double_rice,JPN,-7.467597246170044 +double_rice,JPN,-7.0613017082214355 double_rice,KEN,7.5 -double_rice,KHM,-4.353095293045044 -double_rice,KOR,7.5 -double_rice,LAO,-2.310716390609741 +double_rice,KHM,-2.5964460372924805 +double_rice,KOR,0.0 +double_rice,LAO,-2.5472428798675537 double_rice,LBR,7.5 -double_rice,LKA,-0.44917234778404236 -double_rice,LSO,0.0 +double_rice,LKA,3.3557786345481873 +double_rice,LSO,-0.0 double_rice,MAR,7.5 -double_rice,MDG,3.66713098436594 +double_rice,MDG,3.56772917509079 double_rice,MEX,7.5 double_rice,MKD,7.5 -double_rice,MLI,-7.5 -double_rice,MMR,-2.5912954807281494 +double_rice,MLI,-0.2963952124118805 +double_rice,MMR,-2.7548938989639282 double_rice,MOZ,7.5 -double_rice,MRT,-7.5 +double_rice,MRT,-1.7550534009933472 double_rice,MUS,7.5 -double_rice,MWI,-7.5 -double_rice,MYS,-1.0528628826141357 +double_rice,MWI,-1.9202125668525696 +double_rice,MYS,-0.9602201282978058 double_rice,NAM,7.5 double_rice,NER,7.5 -double_rice,NGA,-3.098779320716858 -double_rice,NIC,5.87060022354126 -double_rice,NPL,5.132853031158447 +double_rice,NGA,-0.4731685668230057 +double_rice,NIC,0.3596424087882042 +double_rice,NPL,3.6091630309820175 double_rice,PAK,7.5 -double_rice,PAN,-1.8307443857192993 +double_rice,PAN,-2.171377658843994 double_rice,PER,7.5 -double_rice,PHL,-1.3794662952423096 +double_rice,PHL,-1.6855337619781494 double_rice,PNG,7.5 double_rice,PRI,7.5 double_rice,PRT,7.5 double_rice,PRY,7.5 -double_rice,RWA,-3.050174117088318 +double_rice,RWA,-3.4891295433044434 double_rice,SAU,7.5 double_rice,SDN,7.5 -double_rice,SEN,-2.742427185177803 +double_rice,SEN,0.19506226480007172 double_rice,SLB,7.5 -double_rice,SLE,-7.255029201507568 +double_rice,SLE,3.5841999650001526 double_rice,SLV,7.5 double_rice,SOM,7.5 double_rice,SSD,7.5 double_rice,SUR,7.5 double_rice,SWZ,7.5 double_rice,SYR,7.5 -double_rice,TCD,-0.93898606300354 -double_rice,TGO,-0.04446191340684891 -double_rice,THA,-0.8102104067802429 +double_rice,TCD,-1.1089786291122437 +double_rice,TGO,-0.26930662244558334 +double_rice,THA,-0.8625509738922119 double_rice,TJK,7.5 double_rice,TKM,7.5 double_rice,TLS,7.5 double_rice,TTO,7.5 -double_rice,TUN,0.0 +double_rice,TUN,-0.0 double_rice,TUR,7.5 -double_rice,TWN,-1.1616821885108948 -double_rice,TZA,4.307738244533539 +double_rice,TWN,-1.8933584690093994 +double_rice,TZA,7.5 double_rice,UGA,7.5 double_rice,URY,7.5 double_rice,USA,7.5 double_rice,UZB,7.5 double_rice,VEN,7.5 -double_rice,VNM,-1.7286503314971924 -double_rice,YEM,0.0 +double_rice,VNM,-2.0124760270118713 +double_rice,YEM,-0.0 double_rice,ZAF,7.5 double_rice,ZMB,7.5 double_rice,ZWE,7.5 -maize_soybean,AFG,-0.18903027474880219 -maize_soybean,AGO,-0.3301544785499573 +maize_soybean,AFG,-0.3637601435184479 +maize_soybean,AGO,-1.5165324211120605 maize_soybean,ALB,7.5 -maize_soybean,ARG,7.5 +maize_soybean,ARG,-0.13341142237186432 maize_soybean,ARM,7.5 -maize_soybean,AUS,1.764742136001587 -maize_soybean,AUT,7.5 -maize_soybean,AZE,4.357408821582794 -maize_soybean,BDI,-1.053770124912262 +maize_soybean,AUS,1.4551827907562256 +maize_soybean,AUT,4.542241096496582 +maize_soybean,AZE,0.20932435989379883 +maize_soybean,BDI,-1.1280118227005005 maize_soybean,BEL,7.5 -maize_soybean,BEN,-0.8169119656085968 -maize_soybean,BFA,-0.4642575979232788 +maize_soybean,BEN,-0.8633319735527039 +maize_soybean,BFA,-0.6894935369491577 maize_soybean,BGD,7.5 maize_soybean,BGR,7.5 maize_soybean,BIH,7.5 -maize_soybean,BLZ,-1.3509411811828613 -maize_soybean,BOL,-0.17573681473731995 -maize_soybean,BRA,0.027264581993222237 -maize_soybean,BTN,1.0892248153686523 -maize_soybean,BWA,3.902852773666382 -maize_soybean,CAF,-1.5961941480636597 -maize_soybean,CAN,7.5 +maize_soybean,BLZ,-1.5307040214538574 +maize_soybean,BOL,3.58927221596241 +maize_soybean,BRA,-0.3273313641548157 +maize_soybean,BTN,3.9479890763759613 +maize_soybean,BWA,0.005076378118246794 +maize_soybean,CAF,-7.5 +maize_soybean,CAN,-0.6854190826416016 maize_soybean,CHE,7.5 -maize_soybean,CHN,-1.6655081510543823 -maize_soybean,CIV,-0.9765808284282684 -maize_soybean,CMR,-1.1323017477989197 -maize_soybean,COD,-1.6054496765136719 -maize_soybean,COG,-1.5415217876434326 -maize_soybean,COL,-0.4117032289505005 -maize_soybean,CRI,-0.8962375521659851 +maize_soybean,CHN,-1.8604493141174316 +maize_soybean,CIV,-1.049591302871704 +maize_soybean,CMR,-1.1731147766113281 +maize_soybean,COD,-1.6656856536865234 +maize_soybean,COG,-1.5515117645263672 +maize_soybean,COL,-0.5583343803882599 +maize_soybean,CRI,-1.06788170337677 maize_soybean,CUB,7.5 maize_soybean,CZE,7.5 maize_soybean,DEU,7.5 -maize_soybean,DZA,0.0 -maize_soybean,ECU,-0.6328637003898621 -maize_soybean,EGY,-1.9893345832824707 -maize_soybean,ESP,7.5 -maize_soybean,ETH,-0.001739787869155407 +maize_soybean,DZA,-0.0 +maize_soybean,ECU,-0.8385081887245178 +maize_soybean,EGY,-2.272494316101074 +maize_soybean,ESP,-1.1886811256408691 +maize_soybean,ETH,-0.25956258177757263 maize_soybean,FRA,7.5 -maize_soybean,GAB,-1.2152018547058105 -maize_soybean,GEO,0.1554230898618698 -maize_soybean,GHA,-0.7944550216197968 +maize_soybean,GAB,-1.3096975684165955 +maize_soybean,GEO,-0.011137900874018669 +maize_soybean,GHA,-0.9089171588420868 maize_soybean,GIN,7.5 -maize_soybean,GRC,3.332735449075699 -maize_soybean,GTM,2.6926229000091553 -maize_soybean,HND,-0.32529017329216003 +maize_soybean,GRC,2.8364598155021667 +maize_soybean,GTM,-0.7565690875053406 +maize_soybean,HND,-0.5347414016723633 maize_soybean,HRV,7.5 -maize_soybean,HUN,7.5 -maize_soybean,IDN,0.41661420464515686 -maize_soybean,IND,-0.6923241168260574 -maize_soybean,IRN,-0.6999404430389404 -maize_soybean,IRQ,-0.48491914197802544 +maize_soybean,HUN,0.2438574582338333 +maize_soybean,IDN,0.010410172864794731 +maize_soybean,IND,-0.5793569087982178 +maize_soybean,IRN,-0.6512221097946167 +maize_soybean,IRQ,-0.7892634123563766 maize_soybean,ISR,7.5 maize_soybean,ITA,7.5 -maize_soybean,JOR,0.0 +maize_soybean,JOR,-0.0 maize_soybean,KAZ,7.5 -maize_soybean,KEN,-0.4269101023674011 -maize_soybean,KGZ,3.6940583027899265 -maize_soybean,KHM,-0.5335918366909027 -maize_soybean,KOR,-0.2164778858423233 -maize_soybean,LAO,0.043448932468891144 +maize_soybean,KEN,-0.4863159954547882 +maize_soybean,KGZ,-0.46937456727027893 +maize_soybean,KHM,-0.7495225369930267 +maize_soybean,KOR,-0.49779826402664185 +maize_soybean,LAO,-0.23618396371603012 maize_soybean,LBN,7.5 -maize_soybean,LKA,-0.8782811760902405 +maize_soybean,LKA,-1.0565711855888367 maize_soybean,LSO,7.5 maize_soybean,LUX,7.5 maize_soybean,MAR,7.5 maize_soybean,MDA,7.5 -maize_soybean,MEX,2.179497241973877 +maize_soybean,MEX,-0.09972605854272842 maize_soybean,MKD,7.5 -maize_soybean,MLI,-0.6292112469673157 -maize_soybean,MMR,-0.9021268486976624 +maize_soybean,MLI,-0.8281409740447998 +maize_soybean,MMR,-1.1019965410232544 maize_soybean,MNE,7.5 -maize_soybean,MOZ,7.5 -maize_soybean,MWI,-0.2680986821651459 -maize_soybean,NGA,-0.7314013838768005 -maize_soybean,NIC,1.3897043466567993 +maize_soybean,MOZ,3.766202434897423 +maize_soybean,MWI,3.574208125472069 +maize_soybean,NGA,-0.8216919898986816 +maize_soybean,NIC,-0.42770321667194366 maize_soybean,NLD,7.5 -maize_soybean,NPL,7.5 -maize_soybean,PAK,-0.3264614902436733 +maize_soybean,NPL,3.6286289766430855 +maize_soybean,PAK,-0.8721129596233368 maize_soybean,PER,7.5 maize_soybean,POL,7.5 maize_soybean,PRT,7.5 -maize_soybean,PRY,-0.22252461314201355 -maize_soybean,ROU,4.383970379829407 +maize_soybean,PRY,-0.5639991760253906 +maize_soybean,ROU,3.9618934094905853 maize_soybean,RUS,7.5 -maize_soybean,RWA,-0.3237958699464798 -maize_soybean,SDN,-0.1569017469882965 -maize_soybean,SLV,-0.8325892686843872 +maize_soybean,RWA,-0.39186209440231323 +maize_soybean,SDN,-0.6063498258590698 +maize_soybean,SLV,-0.9797629415988922 maize_soybean,SRB,7.5 maize_soybean,SSD,7.5 maize_soybean,SVK,7.5 maize_soybean,SVN,7.5 -maize_soybean,SWZ,-0.7677897810935974 +maize_soybean,SWZ,-0.899118959903717 maize_soybean,SYR,7.5 -maize_soybean,TCD,-0.3061584234237671 -maize_soybean,TGO,-0.365085169672966 -maize_soybean,THA,-0.3549405187368393 +maize_soybean,TCD,3.510473385453224 +maize_soybean,TGO,-0.4266141951084137 +maize_soybean,THA,-0.5974937677383423 maize_soybean,TJK,7.5 -maize_soybean,TKM,4.443183600902557 -maize_soybean,TLS,7.5 +maize_soybean,TKM,7.5 +maize_soybean,TLS,4.0747289955616 maize_soybean,TUR,7.5 -maize_soybean,TWN,0.0 -maize_soybean,TZA,-0.26504120230674744 -maize_soybean,UGA,-1.386080265045166 +maize_soybean,TWN,-0.0 +maize_soybean,TZA,-0.4159565716981888 +maize_soybean,UGA,-1.4297746419906616 maize_soybean,UKR,7.5 -maize_soybean,URY,-0.33979201316833496 -maize_soybean,USA,-0.45227915048599243 +maize_soybean,URY,-0.20672881603240967 +maize_soybean,USA,-0.8845836520195007 maize_soybean,UZB,7.5 -maize_soybean,VEN,-0.7985731959342957 -maize_soybean,VNM,-0.16958361864089966 -maize_soybean,ZAF,2.642836332321167 -maize_soybean,ZMB,2.1038013696670532 -maize_soybean,ZWE,1.4439507126808167 -rice_maize,AFG,-1.4417428970336914 -rice_maize,AGO,-0.08673781901597977 -rice_maize,ALB,0.0 +maize_soybean,VEN,-0.9502752423286438 +maize_soybean,VNM,-0.4805498272180557 +maize_soybean,ZAF,-0.31235234439373016 +maize_soybean,ZMB,-0.6437894999980927 +maize_soybean,ZWE,0.8828884959220886 +rice_maize,AFG,-1.7254143953323364 +rice_maize,AGO,-2.3706471920013428 +rice_maize,ALB,-0.0 rice_maize,ARG,7.5 -rice_maize,ARM,0.0 -rice_maize,AUS,7.5 -rice_maize,AZE,7.5 -rice_maize,BDI,-7.5 -rice_maize,BEN,-1.5544970035552979 -rice_maize,BGD,0.5139995068311691 +rice_maize,ARM,-0.0 +rice_maize,AUS,-2.181629180908203 +rice_maize,AZE,-3.527411937713623 +rice_maize,BDI,-0.7213936448097229 +rice_maize,BEN,-1.6537254452705383 +rice_maize,BGD,0.6719281077384949 rice_maize,BGR,7.5 -rice_maize,BLZ,-2.317697763442993 +rice_maize,BLZ,-2.5882227420806885 rice_maize,BOL,7.5 -rice_maize,BRA,0.13870170712471008 -rice_maize,BTN,0.8440825194120407 -rice_maize,BWA,0.0 -rice_maize,CAF,-2.3169145584106445 -rice_maize,CAN,0.0 -rice_maize,CHL,-1.1006411910057068 -rice_maize,CHN,-2.5344632863998413 -rice_maize,CIV,-0.7863163352012634 -rice_maize,CMR,-1.5311205387115479 -rice_maize,COD,-2.4407894611358643 -rice_maize,COL,-1.5295484066009521 -rice_maize,COM,-2.315112590789795 +rice_maize,BRA,-0.18532046675682068 +rice_maize,BTN,-0.7807840406894684 +rice_maize,BWA,-0.0 +rice_maize,CAF,-2.386669635772705 +rice_maize,CAN,-0.0 +rice_maize,CHL,-1.781331181526184 +rice_maize,CHN,-3.118068218231201 +rice_maize,CIV,-0.9498778581619263 +rice_maize,CMR,7.5 +rice_maize,COD,-2.5151937007904053 +rice_maize,COL,-1.9840177297592163 +rice_maize,COM,-2.3835904598236084 rice_maize,CUB,7.5 -rice_maize,DOM,-1.5913703441619873 +rice_maize,DOM,2.363918662071228 rice_maize,DZA,7.5 -rice_maize,ECU,-0.24355557560920715 -rice_maize,EGY,-1.5326589345932007 +rice_maize,ECU,-0.6062188744544983 +rice_maize,EGY,-2.7201571464538574 rice_maize,ERI,7.5 -rice_maize,ESP,-0.9732767343521118 +rice_maize,ESP,-3.4109416007995605 rice_maize,ETH,7.5 rice_maize,FRA,7.5 rice_maize,GEO,7.5 -rice_maize,GHA,-0.8649216294288635 -rice_maize,GIN,-0.7394095063209534 -rice_maize,GNB,-7.5 -rice_maize,GRC,-1.3736534714698792 -rice_maize,GTM,2.3227310180664062 -rice_maize,HND,-1.6148372888565063 -rice_maize,HTI,-2.080762505531311 +rice_maize,GHA,-1.0155074894428253 +rice_maize,GIN,-0.684197187423706 +rice_maize,GNB,-1.6221559047698975 +rice_maize,GRC,-2.7906360626220703 +rice_maize,GTM,-2.216136932373047 +rice_maize,HND,-1.7721517086029053 +rice_maize,HTI,-2.207491397857666 rice_maize,HUN,7.5 -rice_maize,IDN,0.0 -rice_maize,IND,0.07284808903932571 -rice_maize,IRN,-3.7375322580337524 -rice_maize,IRQ,-2.032711148262024 -rice_maize,ITA,-2.783283829689026 -rice_maize,KAZ,0.6285150051116943 -rice_maize,KEN,-2.693563222885132 -rice_maize,KGZ,-2.9307796955108643 -rice_maize,KOR,-6.474852085113525 -rice_maize,LAO,-1.099143147468567 -rice_maize,LKA,-0.9970139861106873 -rice_maize,LSO,0.0 -rice_maize,MAR,-0.18347854539752007 +rice_maize,IDN,-0.0 +rice_maize,IND,-1.1106226444244385 +rice_maize,IRN,-4.246026992797852 +rice_maize,IRQ,-2.36727511882782 +rice_maize,ITA,-3.0518001317977905 +rice_maize,KAZ,-0.9933299422264099 +rice_maize,KEN,-1.2865784764289856 +rice_maize,KGZ,-3.3919849395751953 +rice_maize,KOR,-6.837083578109741 +rice_maize,LAO,-1.5965595245361328 +rice_maize,LKA,-1.2170969247817993 +rice_maize,LSO,-0.0 +rice_maize,MAR,2.6333963871002197 rice_maize,MDA,7.5 -rice_maize,MDG,-1.121085524559021 -rice_maize,MEX,5.394611835479736 +rice_maize,MDG,-1.2474266290664673 +rice_maize,MEX,-0.519189178943634 rice_maize,MKD,7.5 -rice_maize,MLI,-3.8538168370723724 -rice_maize,MMR,-1.7492262125015259 -rice_maize,MOZ,-0.5586673021316528 +rice_maize,MLI,-0.5910901427268982 +rice_maize,MMR,-1.850481390953064 +rice_maize,MOZ,-0.5015707612037659 rice_maize,MUS,7.5 -rice_maize,MWI,-4.375118672847748 +rice_maize,MWI,-1.2465201616287231 rice_maize,NAM,7.5 -rice_maize,NGA,-0.5389896035194397 -rice_maize,NIC,-0.5032784044742584 -rice_maize,NPL,-0.8382139205932617 -rice_maize,PAK,0.2885686755180359 -rice_maize,PER,-3.8790206909179688 -rice_maize,PHL,-0.7126205563545227 +rice_maize,NGA,-0.7111078500747681 +rice_maize,NIC,-0.0 +rice_maize,NPL,-0.5173944532871246 +rice_maize,PAK,-1.424728274345398 +rice_maize,PER,-2.36906361579895 +rice_maize,PHL,-1.1037707924842834 rice_maize,PRI,7.5 -rice_maize,PRT,3.2787038683891296 -rice_maize,PRY,-0.45439404249191284 -rice_maize,ROU,3.2799732387065887 +rice_maize,PRT,2.988905191421509 +rice_maize,PRY,-1.077894926071167 +rice_maize,ROU,-1.5070655941963196 rice_maize,RUS,7.5 -rice_maize,RWA,-1.786763310432434 +rice_maize,RWA,-2.056783676147461 rice_maize,SAU,7.5 -rice_maize,SDN,-2.4222562313079834 +rice_maize,SDN,-2.5225976705551147 rice_maize,SEN,7.5 -rice_maize,SLE,3.625309444963932 -rice_maize,SLV,-2.999232769012451 -rice_maize,SOM,-7.0224387645721436 -rice_maize,SSD,-2.288255214691162 -rice_maize,SVK,0.0 -rice_maize,SVN,0.0 +rice_maize,SLE,7.5 +rice_maize,SLV,-2.8895020484924316 +rice_maize,SOM,-0.4468875229358673 +rice_maize,SSD,-2.357115387916565 +rice_maize,SVK,-0.0 +rice_maize,SVN,-0.0 rice_maize,SWZ,7.5 -rice_maize,SYR,0.48557618260383606 -rice_maize,TCD,-0.3451007902622223 -rice_maize,TGO,-0.27118003368377686 -rice_maize,THA,-0.6752943396568298 -rice_maize,TJK,-2.6985824704170227 -rice_maize,TKM,-1.0258724689483643 -rice_maize,TLS,-1.3125015497207642 -rice_maize,TUN,0.0 -rice_maize,TUR,-2.069835662841797 -rice_maize,TZA,0.0 -rice_maize,UGA,-2.1919249296188354 +rice_maize,SYR,-0.23338383436203003 +rice_maize,TCD,-0.5543370246887207 +rice_maize,TGO,-0.4559953808784485 +rice_maize,THA,-0.9153203964233398 +rice_maize,TJK,-3.795152425765991 +rice_maize,TKM,-1.270441710948944 +rice_maize,TLS,-1.3026959896087646 +rice_maize,TUN,-0.0 +rice_maize,TUR,-2.4413100481033325 +rice_maize,TZA,-0.05638634040951729 +rice_maize,UGA,-2.277077317237854 rice_maize,UKR,7.5 -rice_maize,URY,-0.10472245514392853 -rice_maize,USA,7.5 -rice_maize,UZB,-5.992863893508911 -rice_maize,VEN,-0.8770258128643036 -rice_maize,VNM,-0.8021101355552673 -rice_maize,YEM,0.0 -rice_maize,ZAF,5.006124019622803 -rice_maize,ZMB,-7.5 +rice_maize,URY,-0.6884299516677856 +rice_maize,USA,-1.7078514099121094 +rice_maize,UZB,-5.904582977294922 +rice_maize,VEN,-1.1892468333244324 +rice_maize,VNM,-1.2525776624679565 +rice_maize,YEM,-0.0 +rice_maize,ZAF,-1.193888247013092 +rice_maize,ZMB,-1.0817012786865234 rice_maize,ZWE,7.5 -rice_wheat,AFG,-1.1142328977584839 -rice_wheat,AGO,4.096597284078598 -rice_wheat,ALB,0.0 +rice_wheat,AFG,-1.3595799207687378 +rice_wheat,AGO,7.5 +rice_wheat,ALB,-0.0 rice_wheat,ARG,7.5 -rice_wheat,ARM,0.0 -rice_wheat,AUS,-7.5 -rice_wheat,AZE,7.5 -rice_wheat,BGD,-0.3995904177427292 -rice_wheat,BGR,7.5 +rice_wheat,ARM,-0.0 +rice_wheat,AUS,-2.0604162216186523 +rice_wheat,AZE,-3.140916109085083 +rice_wheat,BGD,-0.693154513835907 +rice_wheat,BGR,2.8220043778419495 rice_wheat,BOL,7.5 rice_wheat,BRA,7.5 -rice_wheat,BTN,7.5 -rice_wheat,BWA,0.0 -rice_wheat,CAN,0.0 -rice_wheat,CHL,-0.7711638808250427 -rice_wheat,CHN,-2.575352430343628 -rice_wheat,CMR,-1.5374877452850342 -rice_wheat,COD,-2.1612181663513184 +rice_wheat,BTN,-0.4830802381038666 +rice_wheat,BWA,-0.0 +rice_wheat,CAN,-0.0 +rice_wheat,CHL,-1.281676173210144 +rice_wheat,CHN,-3.024598717689514 +rice_wheat,CMR,-1.7146903276443481 +rice_wheat,COD,-2.2412753105163574 rice_wheat,DZA,7.5 -rice_wheat,ECU,-1.2879406213760376 -rice_wheat,EGY,-0.9247386455535889 -rice_wheat,ESP,-0.6620495915412903 +rice_wheat,ECU,-1.3063002824783325 +rice_wheat,EGY,-2.394174098968506 +rice_wheat,ESP,-2.19299054145813 rice_wheat,ETH,7.5 rice_wheat,FRA,7.5 rice_wheat,GEO,7.5 -rice_wheat,GRC,-0.43876534700393677 -rice_wheat,GTM,0.0 -rice_wheat,HND,-1.9596569538116455 +rice_wheat,GRC,-1.4912328124046326 +rice_wheat,GTM,7.5 +rice_wheat,HND,-2.0529308319091797 rice_wheat,HUN,7.5 -rice_wheat,IND,1.573748528957367 -rice_wheat,IRN,-3.188071608543396 -rice_wheat,IRQ,-1.8810048699378967 -rice_wheat,ITA,-2.728669047355652 -rice_wheat,JPN,-1.294859528541565 -rice_wheat,KAZ,-0.10879222303628922 -rice_wheat,KGZ,-3.064410090446472 -rice_wheat,KOR,7.5 -rice_wheat,LAO,0.0 -rice_wheat,LSO,0.0 -rice_wheat,MAR,-0.622918963432312 +rice_wheat,IND,-1.0325279235839844 +rice_wheat,IRN,-3.5437495708465576 +rice_wheat,IRQ,-2.0931166410446167 +rice_wheat,ITA,-2.4195598363876343 +rice_wheat,JPN,-0.9091683030128479 +rice_wheat,KAZ,-1.054283857345581 +rice_wheat,KGZ,-3.27589750289917 +rice_wheat,KOR,-6.689305782318115 +rice_wheat,LAO,-0.0 +rice_wheat,LSO,-0.0 +rice_wheat,MAR,-2.4089279174804688 rice_wheat,MDA,7.5 -rice_wheat,MDG,-0.8242219090461731 +rice_wheat,MDG,-0.9571394920349121 rice_wheat,MEX,7.5 rice_wheat,MKD,7.5 -rice_wheat,MLI,0.05432529002428055 -rice_wheat,MMR,-1.964646577835083 +rice_wheat,MLI,-0.5888196229934692 +rice_wheat,MMR,-1.8358181715011597 rice_wheat,MOZ,7.5 rice_wheat,MRT,7.5 -rice_wheat,MWI,7.5 -rice_wheat,NAM,7.5 -rice_wheat,NER,-7.5 -rice_wheat,NGA,-7.5 -rice_wheat,NPL,3.393115282058716 -rice_wheat,PAK,-0.13051317632198334 -rice_wheat,PRT,-0.9750400185585022 -rice_wheat,PRY,-0.5190354287624359 +rice_wheat,MWI,2.8601826429367065 +rice_wheat,NAM,2.692684054374695 +rice_wheat,NER,-1.397388756275177 +rice_wheat,NGA,-0.8274063467979431 +rice_wheat,NPL,-0.7158598005771637 +rice_wheat,PAK,-1.44150972366333 +rice_wheat,PRT,-1.3273289203643799 +rice_wheat,PRY,-0.9229036867618561 rice_wheat,ROU,7.5 rice_wheat,RUS,7.5 -rice_wheat,RWA,-1.969196081161499 -rice_wheat,SDN,-7.5 -rice_wheat,SOM,-7.5 -rice_wheat,SVK,0.0 -rice_wheat,SVN,0.0 +rice_wheat,RWA,-2.214703321456909 +rice_wheat,SDN,-1.564651370048523 +rice_wheat,SOM,7.5 +rice_wheat,SVK,-0.0 +rice_wheat,SVN,-0.0 rice_wheat,SWZ,7.5 -rice_wheat,SYR,0.09349346905946732 -rice_wheat,TCD,-6.476876258850098 +rice_wheat,SYR,-0.490796834230423 +rice_wheat,TCD,-0.8735635578632355 rice_wheat,THA,7.5 -rice_wheat,TJK,-2.811855733394623 -rice_wheat,TKM,-1.210829734802246 -rice_wheat,TUN,0.0 -rice_wheat,TUR,-2.0662403106689453 -rice_wheat,TZA,-0.1595538780093193 -rice_wheat,UGA,-1.9200326204299927 +rice_wheat,TJK,-3.7066290378570557 +rice_wheat,TKM,-1.3613498210906982 +rice_wheat,TUN,-0.0 +rice_wheat,TUR,-2.2071304321289062 +rice_wheat,TZA,-0.48743367195129395 +rice_wheat,UGA,-2.0336015224456787 rice_wheat,UKR,7.5 -rice_wheat,URY,-0.02746044471859932 -rice_wheat,USA,7.5 -rice_wheat,UZB,-5.090646266937256 +rice_wheat,URY,-0.607020765542984 +rice_wheat,USA,-1.4283429980278015 +rice_wheat,UZB,-4.8279805183410645 rice_wheat,VEN,7.5 -rice_wheat,VNM,-1.170015573501587 -rice_wheat,ZAF,4.613178253173828 -rice_wheat,ZMB,-7.269059658050537 +rice_wheat,VNM,-1.4757278561592102 +rice_wheat,ZAF,-1.3662726879119873 +rice_wheat,ZMB,-0.8025999069213867 rice_wheat,ZWE,7.5 triple_rice,AFG,7.5 triple_rice,AGO,7.5 @@ -496,13 +497,13 @@ triple_rice,AUS,7.5 triple_rice,BDI,7.5 triple_rice,BEN,7.5 triple_rice,BFA,7.5 -triple_rice,BGD,0.046352850273251534 +triple_rice,BGD,-0.4842730015516281 triple_rice,BLZ,7.5 triple_rice,BOL,7.5 triple_rice,BRA,7.5 triple_rice,BRN,7.5 -triple_rice,BTN,-2.8238296508789062 -triple_rice,BWA,0.0 +triple_rice,BTN,-3.1066852807998657 +triple_rice,BWA,-0.0 triple_rice,CHN,7.5 triple_rice,CIV,7.5 triple_rice,CMR,7.5 @@ -526,8 +527,8 @@ triple_rice,GMB,7.5 triple_rice,GNB,7.5 triple_rice,GNQ,7.5 triple_rice,GRC,7.5 -triple_rice,GTM,7.5 -triple_rice,GUF,0.0 +triple_rice,GTM,1.568363904953003 +triple_rice,GUF,7.5 triple_rice,GUY,7.5 triple_rice,HND,7.5 triple_rice,HTI,7.5 @@ -537,7 +538,7 @@ triple_rice,IRN,7.5 triple_rice,IRQ,7.5 triple_rice,ITA,7.5 triple_rice,KEN,7.5 -triple_rice,KHM,-7.5 +triple_rice,KHM,-3.8946691751480103 triple_rice,LAO,7.5 triple_rice,LBR,7.5 triple_rice,LKA,7.5 @@ -567,7 +568,7 @@ triple_rice,RWA,7.5 triple_rice,SAU,7.5 triple_rice,SDN,7.5 triple_rice,SEN,7.5 -triple_rice,SLB,-4.267874717712402 +triple_rice,SLB,-4.461336374282837 triple_rice,SLE,7.5 triple_rice,SLV,7.5 triple_rice,SOM,7.5 @@ -577,7 +578,7 @@ triple_rice,SWZ,7.5 triple_rice,TCD,7.5 triple_rice,TGO,7.5 triple_rice,THA,7.5 -triple_rice,TLS,7.5 +triple_rice,TLS,1.2680587768554688 triple_rice,TTO,7.5 triple_rice,TUR,7.5 triple_rice,TWN,7.5 @@ -586,204 +587,204 @@ triple_rice,UGA,7.5 triple_rice,URY,7.5 triple_rice,USA,7.5 triple_rice,VEN,7.5 -triple_rice,VNM,7.5 -triple_rice,YEM,0.0 +triple_rice,VNM,2.5807573795318604 +triple_rice,YEM,-0.0 triple_rice,ZAF,7.5 triple_rice,ZMB,7.5 triple_rice,ZWE,7.5 -wheat_maize,AFG,-0.9223511517047882 -wheat_maize,AGO,0.06085094437003136 +wheat_maize,AFG,-0.9849343299865723 +wheat_maize,AGO,-1.5589878559112549 wheat_maize,ALB,7.5 wheat_maize,ARE,7.5 wheat_maize,ARG,7.5 -wheat_maize,ARM,0.13379235565662384 -wheat_maize,AUS,0.7673986256122589 +wheat_maize,ARM,-0.20182248950004578 +wheat_maize,AUS,0.22560419142246246 wheat_maize,AUT,7.5 -wheat_maize,AZE,0.21032091975212097 -wheat_maize,BDI,-1.0480952262878418 +wheat_maize,AZE,-0.6330894827842712 +wheat_maize,BDI,-1.127558708190918 wheat_maize,BEL,7.5 -wheat_maize,BGD,-0.06975655257701874 +wheat_maize,BGD,0.10837168991565704 wheat_maize,BGR,7.5 wheat_maize,BIH,7.5 -wheat_maize,BOL,7.5 -wheat_maize,BRA,0.05257541500031948 -wheat_maize,BTN,1.7523919343948364 -wheat_maize,BWA,3.8771475106477737 +wheat_maize,BOL,-0.5363734364509583 +wheat_maize,BRA,-0.18438968807458878 +wheat_maize,BTN,0.9781211018562317 +wheat_maize,BWA,-0.10034539550542831 wheat_maize,CAN,7.5 wheat_maize,CHE,7.5 -wheat_maize,CHL,-0.6160206198692322 -wheat_maize,CHN,-2.0744786262512207 -wheat_maize,CMR,-0.6157926917076111 -wheat_maize,COD,-1.461105227470398 -wheat_maize,COL,-0.6463930606842041 +wheat_maize,CHL,-1.4138022661209106 +wheat_maize,CHN,-2.4889100790023804 +wheat_maize,CMR,-0.8750249147415161 +wheat_maize,COD,-1.5499111413955688 +wheat_maize,COL,-1.0134680271148682 wheat_maize,CZE,7.5 wheat_maize,DEU,7.5 wheat_maize,DZA,7.5 -wheat_maize,ECU,-1.0442391633987427 -wheat_maize,EGY,-0.6321408748626709 -wheat_maize,ERI,-1.3943330645561218 -wheat_maize,ESP,0.06611741334199905 -wheat_maize,ETH,-0.2691202014684677 +wheat_maize,ECU,-1.1721757650375366 +wheat_maize,EGY,-2.27541184425354 +wheat_maize,ERI,-1.4826164841651917 +wheat_maize,ESP,-0.361922062933445 +wheat_maize,ETH,-0.4375545382499695 wheat_maize,FRA,7.5 -wheat_maize,GEO,-0.1255870759487152 -wheat_maize,GRC,-0.7035238444805145 -wheat_maize,GTM,6.486738681793213 -wheat_maize,HND,-1.047223448753357 +wheat_maize,GEO,0.03534886613488197 +wheat_maize,GRC,2.8829175233840942 +wheat_maize,GTM,3.285066694021225 +wheat_maize,HND,-1.1586399674415588 wheat_maize,HRV,7.5 -wheat_maize,HUN,7.5 -wheat_maize,IND,-0.23743987828493118 -wheat_maize,IRN,-0.9351812601089478 -wheat_maize,IRQ,-1.0056968927383423 -wheat_maize,ISR,-6.639710426330566 -wheat_maize,ITA,0.14105527848005295 -wheat_maize,JOR,1.4564753770828247 -wheat_maize,KAZ,0.41205526888370514 -wheat_maize,KEN,-0.6920293867588043 -wheat_maize,KGZ,0.0 -wheat_maize,KOR,0.08538535237312317 -wheat_maize,LAO,0.0 -wheat_maize,LBN,-0.3937086760997772 +wheat_maize,HUN,-0.1818016991019249 +wheat_maize,IND,-0.5853933691978455 +wheat_maize,IRN,-1.325901746749878 +wheat_maize,IRQ,-1.3061878681182861 +wheat_maize,ISR,-6.799508094787598 +wheat_maize,ITA,3.2091699838638306 +wheat_maize,JOR,-0.4680543839931488 +wheat_maize,KAZ,-0.6769985556602478 +wheat_maize,KEN,-0.8018709123134613 +wheat_maize,KGZ,-0.4710511565208435 +wheat_maize,KOR,-0.32269126176834106 +wheat_maize,LAO,-0.0 +wheat_maize,LBN,-0.6227757036685944 wheat_maize,LBY,7.5 -wheat_maize,LSO,0.12134746462106705 +wheat_maize,LSO,-0.015198908746242523 wheat_maize,LUX,7.5 -wheat_maize,MAR,-0.1560538113117218 +wheat_maize,MAR,-0.5118548274040222 wheat_maize,MDA,7.5 -wheat_maize,MDG,-0.9783177375793457 -wheat_maize,MEX,3.709369421005249 +wheat_maize,MDG,-1.1886930465698242 +wheat_maize,MEX,-0.8947439193725586 wheat_maize,MKD,7.5 -wheat_maize,MMR,-1.0011625289916992 +wheat_maize,MMR,-0.5519289374351501 wheat_maize,MNE,7.5 -wheat_maize,MOZ,-0.17766039073467255 -wheat_maize,MWI,7.5 -wheat_maize,NAM,-5.040892124176025 +wheat_maize,MOZ,-0.27184000611305237 +wheat_maize,MWI,3.3756099939346313 +wheat_maize,NAM,-1.0349177122116089 wheat_maize,NIC,7.5 wheat_maize,NLD,7.5 -wheat_maize,NPL,0.5430206060409546 -wheat_maize,NZL,-1.0715875327587128 -wheat_maize,OMN,-5.012444019317627 -wheat_maize,PAK,0.6206960678100586 -wheat_maize,PER,-1.0850019454956055 +wheat_maize,NPL,-0.5624594837427139 +wheat_maize,NZL,-1.7070268988609314 +wheat_maize,OMN,-3.4702497720718384 +wheat_maize,PAK,-0.6241451501846313 +wheat_maize,PER,-1.0873700976371765 wheat_maize,POL,7.5 -wheat_maize,PRT,7.5 -wheat_maize,PRY,-0.13775226473808289 -wheat_maize,PSE,2.3259319067001343 +wheat_maize,PRT,-0.3758561760187149 +wheat_maize,PRY,-0.42313165962696075 +wheat_maize,PSE,1.4735210537910461 wheat_maize,ROU,7.5 wheat_maize,RUS,7.5 -wheat_maize,RWA,-0.4318433403968811 -wheat_maize,SAU,-4.549129247665405 -wheat_maize,SLV,0.0 -wheat_maize,SOM,-7.5 +wheat_maize,RWA,-0.5481820106506348 +wheat_maize,SAU,-4.958998918533325 +wheat_maize,SLV,-0.0 +wheat_maize,SOM,0.08026691898703575 wheat_maize,SRB,7.5 wheat_maize,SSD,7.5 wheat_maize,SVK,7.5 wheat_maize,SVN,7.5 -wheat_maize,SWZ,-1.1316282749176025 -wheat_maize,SYR,0.12519118189811707 +wheat_maize,SWZ,-1.235953450202942 +wheat_maize,SYR,-0.3142406642436981 wheat_maize,THA,7.5 -wheat_maize,TJK,-0.10244544222950935 -wheat_maize,TKM,-0.7906519174575806 -wheat_maize,TUN,0.0 -wheat_maize,TUR,0.6864449679851532 -wheat_maize,TWN,0.46693259477615356 -wheat_maize,TZA,-0.13013962656259537 -wheat_maize,UGA,-1.1253196001052856 +wheat_maize,TJK,-0.6639165282249451 +wheat_maize,TKM,-0.8205778002738953 +wheat_maize,TUN,-0.0 +wheat_maize,TUR,0.37567462027072906 +wheat_maize,TWN,-0.008953025564551353 +wheat_maize,TZA,-0.3282812088727951 +wheat_maize,UGA,-1.2599499225616455 wheat_maize,UKR,7.5 -wheat_maize,URY,-0.0017564594745635986 -wheat_maize,USA,-0.067537322640419 -wheat_maize,UZB,-1.6876318454742432 +wheat_maize,URY,-0.4269259050488472 +wheat_maize,USA,-0.7423370778560638 +wheat_maize,UZB,-1.9013787508010864 wheat_maize,VEN,7.5 -wheat_maize,VNM,0.027547618374228477 -wheat_maize,YEM,-7.5 -wheat_maize,ZAF,4.6093175411224365 -wheat_maize,ZMB,-7.151492118835449 -wheat_maize,ZWE,6.538405895233154 -wheat_soybean,AFG,0.04527095705270767 -wheat_soybean,AGO,-0.08519302308559418 +wheat_maize,VNM,-0.46025991439819336 +wheat_maize,YEM,-1.6670135855674744 +wheat_maize,ZAF,-0.3842948079109192 +wheat_maize,ZMB,0.3949296474456787 +wheat_maize,ZWE,7.5 +wheat_soybean,AFG,0.023451562970876694 +wheat_soybean,AGO,-1.297289252281189 wheat_soybean,ALB,7.5 wheat_soybean,ARG,7.5 wheat_soybean,ARM,7.5 -wheat_soybean,AUS,1.035093069076538 -wheat_soybean,AUT,7.5 -wheat_soybean,AZE,7.5 -wheat_soybean,BDI,-1.4691311120986938 +wheat_soybean,AUS,0.8355346322059631 +wheat_soybean,AUT,4.0272236764431 +wheat_soybean,AZE,0.6157877445220947 +wheat_soybean,BDI,-1.5064332485198975 wheat_soybean,BEL,7.5 -wheat_soybean,BGD,3.5620241463184357 +wheat_soybean,BGD,3.5075069069862366 wheat_soybean,BGR,7.5 wheat_soybean,BIH,7.5 wheat_soybean,BOL,7.5 -wheat_soybean,BRA,-0.20813433825969696 +wheat_soybean,BRA,-0.31569650769233704 wheat_soybean,BTN,7.5 wheat_soybean,BWA,7.5 wheat_soybean,CAN,7.5 wheat_soybean,CHE,7.5 -wheat_soybean,CHN,-1.4649994373321533 -wheat_soybean,CMR,-0.4691017121076584 -wheat_soybean,COD,-1.2634633779525757 +wheat_soybean,CHN,-1.6905781626701355 +wheat_soybean,CMR,-0.6206047534942627 +wheat_soybean,COD,-1.3283783197402954 wheat_soybean,CZE,7.5 wheat_soybean,DEU,7.5 -wheat_soybean,DZA,0.0 -wheat_soybean,ECU,-0.6779110133647919 -wheat_soybean,EGY,-1.3776955604553223 -wheat_soybean,ESP,7.5 -wheat_soybean,ETH,-0.8534783720970154 +wheat_soybean,DZA,-0.0 +wheat_soybean,ECU,-0.7177078425884247 +wheat_soybean,EGY,-1.7534197568893433 +wheat_soybean,ESP,-0.0031415356788784266 +wheat_soybean,ETH,-0.9896889925003052 wheat_soybean,FRA,7.5 -wheat_soybean,GEO,-0.023869957774877548 -wheat_soybean,GRC,3.680923141539097 -wheat_soybean,GTM,2.564176559448242 -wheat_soybean,HND,-0.6847660541534424 +wheat_soybean,GEO,-0.012631510384380817 +wheat_soybean,GRC,3.4658419489860535 +wheat_soybean,GTM,3.733000973239541 +wheat_soybean,HND,-0.9046487808227539 wheat_soybean,HRV,7.5 -wheat_soybean,HUN,7.5 -wheat_soybean,IND,2.3271501064300537 -wheat_soybean,IRN,-0.41254279017448425 -wheat_soybean,IRQ,-0.488378694280982 +wheat_soybean,HUN,0.07651042938232422 +wheat_soybean,IND,-0.45092836022377014 +wheat_soybean,IRN,-0.3359500914812088 +wheat_soybean,IRQ,-0.634438656270504 wheat_soybean,ISR,7.5 wheat_soybean,ITA,7.5 -wheat_soybean,JOR,0.0 -wheat_soybean,JPN,1.4244059324264526 +wheat_soybean,JOR,-0.0 +wheat_soybean,JPN,1.758805274963379 wheat_soybean,KAZ,7.5 -wheat_soybean,KEN,-0.7176618576049805 -wheat_soybean,KGZ,3.626110717654228 -wheat_soybean,KOR,-0.36814725399017334 -wheat_soybean,LAO,0.0 +wheat_soybean,KEN,-0.7852859199047089 +wheat_soybean,KGZ,-0.38569173216819763 +wheat_soybean,KOR,3.4483194947242737 +wheat_soybean,LAO,-0.0 wheat_soybean,LBN,7.5 wheat_soybean,LSO,7.5 wheat_soybean,LUX,7.5 -wheat_soybean,MAR,-0.5912825465202332 +wheat_soybean,MAR,-1.7598727345466614 wheat_soybean,MDA,7.5 -wheat_soybean,MEX,7.5 +wheat_soybean,MEX,-0.4874807149171829 wheat_soybean,MKD,7.5 -wheat_soybean,MMR,-0.6119680404663086 +wheat_soybean,MMR,3.7208532635122538 wheat_soybean,MNE,7.5 wheat_soybean,MOZ,7.5 -wheat_soybean,MWI,7.5 +wheat_soybean,MWI,3.2981008887290955 wheat_soybean,NGA,7.5 wheat_soybean,NLD,7.5 wheat_soybean,NPL,7.5 -wheat_soybean,PAK,-0.08180655911564827 -wheat_soybean,PER,-0.8094130158424377 +wheat_soybean,PAK,-0.6318481862545013 +wheat_soybean,PER,-0.573964536190033 wheat_soybean,POL,7.5 wheat_soybean,PRT,7.5 -wheat_soybean,PRY,-0.10462310910224915 +wheat_soybean,PRY,-0.40803292393684387 wheat_soybean,ROU,7.5 wheat_soybean,RUS,7.5 -wheat_soybean,RWA,-0.44948336482048035 +wheat_soybean,RWA,-0.5008382648229599 wheat_soybean,SRB,7.5 wheat_soybean,SVK,7.5 wheat_soybean,SVN,7.5 -wheat_soybean,SWZ,-0.5193095803260803 +wheat_soybean,SWZ,-0.6631672978401184 wheat_soybean,SYR,7.5 wheat_soybean,THA,7.5 wheat_soybean,TJK,7.5 -wheat_soybean,TKM,0.3698643445968628 +wheat_soybean,TKM,4.054523408412933 wheat_soybean,TUR,7.5 -wheat_soybean,TWN,-0.37869152426719666 -wheat_soybean,TZA,-0.36440300941467285 -wheat_soybean,UGA,-1.134753942489624 +wheat_soybean,TWN,-0.5627845525741577 +wheat_soybean,TZA,-0.5059201717376709 +wheat_soybean,UGA,-1.2213156819343567 wheat_soybean,UKR,7.5 -wheat_soybean,URY,-0.2477397918701172 -wheat_soybean,USA,-0.048939481377601624 +wheat_soybean,URY,-0.11813099682331085 +wheat_soybean,USA,-0.30147039890289307 wheat_soybean,UZB,7.5 -wheat_soybean,VNM,-0.5917498469352722 -wheat_soybean,ZAF,2.420696258544922 -wheat_soybean,ZMB,5.127807140350342 -wheat_soybean,ZWE,4.643855094909668 +wheat_soybean,VNM,-0.8296979069709778 +wheat_soybean,ZAF,-0.5150110721588135 +wheat_soybean,ZMB,-0.4146267771720886 +wheat_soybean,ZWE,4.302780628204346 diff --git a/data/curated/calibration/default/provenance.yaml b/data/curated/calibration/default/provenance.yaml index 900e6b27..8991605c 100644 --- a/data/curated/calibration/default/provenance.yaml +++ b/data/curated/calibration/default/provenance.yaml @@ -4,12 +4,12 @@ # tools/calibrate; do not edit by hand. # Licensing: see the annotation in REUSE.toml. base_config: config/default.yaml -generated_at: '2026-07-21T23:02:35+00:00' -git_commit: 548ef138989c4a0ed28a90adeb0e4626b044a8ed +generated_at: '2026-07-22T19:14:30+00:00' +git_commit: 81a69806335b9eeba703c84240982fd8ba8d131d source: default structural_config: aggregation.irrigated_area_source: current - aggregation.regions.allow_cross_border: false + aggregation.regions.basin_scarcity_weight: 2.0 aggregation.regions.method: kmeans aggregation.regions.target_count: 750 aggregation.resource_class_quantiles: @@ -991,14 +991,27 @@ structural_config: numerics.min_grassland_yield_t_per_ha: 0.05 numerics.min_land_area_ha: 100 numerics.min_link_area_mha: 1.0e-06 + numerics.min_water_capacity_mm3: 1.0e-06 numerics.min_water_requirement_m3_per_ha: 0.1 optimal_taxes.enabled: false residues.max_feed_fraction: 0.3 residues.max_feed_fraction_by_region.Asia: 0.7 residues.max_feed_fraction_by_region.Southern Asia: 0.85 residues.max_feed_fraction_by_region.Sub-Saharan Africa: 0.7 - water.huang_reference_year: 2010 - water.supply_scenario: current_use + water.data.availability: aware + water.data.groundwater_trend_end: 2019 + water.data.groundwater_trend_start: 2000 + water.data.huang_reference_year: 2010 + water.data.surface_reference_end: 2019 + water.data.surface_reference_start: 1990 + water.irrigation.consumed_fraction: 0.58 + water.irrigation.eta_max: 5.0 + water.irrigation.eta_min: 0.2 + water.supply.groundwater: true + water.supply.groundwater_ceiling_factor: 3.0 + water.supply.pumping_cost_usd_per_m3: 0.04 + water.supply.scarcity_tiers: false + water.temporal_resolution: 1 weight_conversion.carcass_to_fresh.meat-cattle: 0.67 weight_conversion.carcass_to_fresh.meat-chicken: 0.6 weight_conversion.carcass_to_fresh.meat-pig: 0.73 diff --git a/data/curated/calibration/gbd-anchored/animal_cost.csv b/data/curated/calibration/gbd-anchored/animal_cost.csv index 646c252e..79543384 100644 --- a/data/curated/calibration/gbd-anchored/animal_cost.csv +++ b/data/curated/calibration/gbd-anchored/animal_cost.csv @@ -1,1226 +1,1226 @@ product,country,correction_bnusd_per_mt_feed -dairy,AFG,0.05948035605251789 -dairy,AGO,-0.22816479951143265 -dairy,ALB,-0.2514187814667821 -dairy,ARE,-0.07126558385789394 -dairy,ARG,-0.1533764898777008 -dairy,ARM,0.019554350525140762 -dairy,ASM,0.0 -dairy,ATG,-0.19369371235370636 -dairy,AUS,-0.04751752142328769 -dairy,AUT,0.06652382202446461 -dairy,AZE,-0.029685435350984335 -dairy,BDI,-0.1172116007655859 -dairy,BEL,0.009280476719141006 -dairy,BEN,-0.20199429243803024 -dairy,BFA,-0.20926211774349213 -dairy,BGD,-0.03136761346831918 -dairy,BGR,0.10831544920802116 -dairy,BHS,-0.12323272600769997 -dairy,BIH,0.1936991885304451 -dairy,BLR,0.003795657306909561 -dairy,BLZ,-0.15386062115430832 -dairy,BOL,-0.17518648505210876 -dairy,BRA,-0.1734568327665329 -dairy,BRB,-0.32947251200675964 -dairy,BRN,-0.10618385300040245 -dairy,BTN,-0.10305097280070186 -dairy,BWA,-0.1914641335606575 -dairy,CAF,-0.15165051445364952 -dairy,CAN,-0.03674883069470525 -dairy,CHE,0.016698447987437248 -dairy,CHL,-0.14888644963502884 -dairy,CHN,0.2091413512825966 -dairy,CIV,-0.20182392001152039 -dairy,CMR,-0.20563890039920807 -dairy,COD,-0.21945477277040482 -dairy,COG,-0.12868252024054527 -dairy,COL,-0.17744148522615433 -dairy,COM,-0.18556278198957443 -dairy,CPV,-0.4468294829130173 -dairy,CRI,-0.04583306610584259 -dairy,CUB,-0.22000718861818314 -dairy,CYP,-0.10864154249429703 -dairy,CZE,0.035562243312597275 -dairy,DEU,0.044712772592902184 -dairy,DJI,-0.029067233204841614 -dairy,DNK,0.07562219724059105 -dairy,DOM,-0.038142599165439606 -dairy,DZA,-0.000805526040494442 -dairy,ECU,-0.2116253599524498 -dairy,EGY,-0.30121560394763947 -dairy,ERI,-0.11299882270395756 -dairy,ESP,-0.04693188238888979 -dairy,EST,0.09174656309187412 -dairy,ETH,-0.14208469539880753 -dairy,FIN,0.15247543156147003 -dairy,FJI,-0.018866688013076782 -dairy,FRA,-0.026401060167700052 -dairy,GAB,-0.06538360100239515 -dairy,GBR,0.06420060805976391 -dairy,GEO,5.1705166697502136e-05 -dairy,GHA,-0.11935435049235821 -dairy,GIN,-0.20570342987775803 -dairy,GMB,-0.1844334378838539 -dairy,GNB,-0.3711504265666008 -dairy,GNQ,0.0 -dairy,GRC,0.013509842567145824 -dairy,GRD,-0.2176344022154808 -dairy,GTM,-0.20474915206432343 -dairy,GUF,0.0 -dairy,GUY,-0.16701003164052963 -dairy,HND,-0.1592087298631668 -dairy,HRV,0.028183121234178543 -dairy,HTI,-0.03947852551937103 -dairy,HUN,0.0512507245875895 -dairy,IDN,-0.043341767974197865 -dairy,IND,-0.3679312504827976 -dairy,IRL,-0.026023900136351585 -dairy,IRN,0.05937865749001503 -dairy,IRQ,-0.11447953805327415 -dairy,ISL,-0.07572447136044502 -dairy,ISR,-0.050561221316456795 -dairy,ITA,-0.0009662844240665436 -dairy,JAM,-0.05449102446436882 -dairy,JOR,0.13855566829442978 -dairy,JPN,0.15582039207220078 -dairy,KAZ,0.115753423422575 -dairy,KEN,-0.09929692186415195 -dairy,KGZ,0.2253614068031311 -dairy,KHM,-0.06400702893733978 -dairy,KOR,0.18839731812477112 -dairy,LAO,-0.029387150891125202 -dairy,LBN,0.13091862946748734 -dairy,LBR,-0.0911652585491538 -dairy,LBY,-0.08772934600710869 -dairy,LKA,0.02084164507687092 -dairy,LSO,-0.10193965956568718 -dairy,LTU,0.10512112081050873 -dairy,LUX,0.010899993823841214 -dairy,LVA,0.08744405768811703 -dairy,MAR,-0.12931741029024124 -dairy,MDA,0.028913515619933605 -dairy,MDG,-0.2109258696436882 -dairy,MEX,-0.11926885694265366 -dairy,MKD,0.023171648383140564 -dairy,MLI,-0.13344931975007057 -dairy,MLT,-0.27288600243628025 -dairy,MMR,-0.031939467415213585 -dairy,MNE,-0.04975227266550064 -dairy,MNG,0.01167712640017271 -dairy,MOZ,-0.1358620524406433 -dairy,MRT,-0.21128926426172256 -dairy,MUS,-0.06032385677099228 -dairy,MWI,-0.12943743914365768 -dairy,MYS,-0.07556125149130821 -dairy,NAM,-0.15970788151025772 -dairy,NER,-0.21360696107149124 -dairy,NGA,-0.11828519590198994 -dairy,NIC,-0.23618263006210327 -dairy,NLD,-0.028704581782221794 -dairy,NOR,0.10295708291232586 -dairy,NPL,-0.27968865633010864 -dairy,NZL,-0.005088516045361757 -dairy,OMN,-0.10393724590539932 -dairy,PAK,0.03161821514368057 -dairy,PAN,-0.21244456619024277 -dairy,PER,-0.17759083211421967 -dairy,PHL,0.06920712254941463 -dairy,PNG,-0.002422492951154709 -dairy,POL,0.06574733555316925 -dairy,PRI,-0.1767854019999504 -dairy,PRT,-0.010223682038486004 -dairy,PRY,-0.14704549312591553 -dairy,PSE,0.08903957530856133 -dairy,ROU,0.11849755048751831 -dairy,RUS,0.10533764399588108 -dairy,RWA,-0.18197890371084213 -dairy,SAU,0.20517781376838684 -dairy,SDN,-0.05368632171303034 -dairy,SEN,-0.4661438539624214 -dairy,SLB,-0.006364775355905294 -dairy,SLE,-0.1024381872266531 -dairy,SLV,-0.1997196227312088 -dairy,SOM,-0.15792151540517807 -dairy,SRB,0.08870055340230465 -dairy,SSD,-0.09512730315327644 -dairy,STP,-0.14004310220479965 -dairy,SUR,-0.18152759224176407 -dairy,SVK,0.05123866396024823 -dairy,SVN,0.050061638467013836 -dairy,SWE,0.12060727551579475 -dairy,SWZ,-0.2142164707183838 -dairy,SYR,-0.005485689267516136 -dairy,TCD,-0.14806589111685753 -dairy,TGO,-0.12115511856973171 -dairy,THA,0.08200897835195065 -dairy,TJK,0.20176243782043457 -dairy,TKM,0.13555236533284187 -dairy,TLS,-0.06912835780531168 -dairy,TTO,-0.13972122222185135 -dairy,TUN,-0.07113808020949364 -dairy,TUR,-0.022594089154154062 -dairy,TWN,0.23127110302448273 -dairy,TZA,-0.1776728257536888 -dairy,UGA,-0.11924964934587479 -dairy,UKR,0.0698440345004201 -dairy,URY,-0.22308798134326935 -dairy,USA,0.003893917426466942 -dairy,UZB,0.14863339811563492 -dairy,VEN,-0.24102213978767395 -dairy,VNM,-0.05842544324696064 -dairy,VUT,0.0 -dairy,YEM,-0.1811002865433693 -dairy,ZAF,-0.2188762128353119 -dairy,ZMB,-0.2391788735985756 -dairy,ZWE,-0.4814244359731674 -dairy-buffalo,AFG,0.0 -dairy-buffalo,AGO,0.0 -dairy-buffalo,ALB,0.0 -dairy-buffalo,ARE,0.0 -dairy-buffalo,ARG,0.0 -dairy-buffalo,ARM,0.0 -dairy-buffalo,ASM,0.0 -dairy-buffalo,ATG,0.0 -dairy-buffalo,AUS,0.0 -dairy-buffalo,AUT,0.0 -dairy-buffalo,AZE,0.0 -dairy-buffalo,BDI,0.0 -dairy-buffalo,BEL,0.0 -dairy-buffalo,BEN,0.0 -dairy-buffalo,BFA,0.0 -dairy-buffalo,BGD,0.2287416011095047 -dairy-buffalo,BGR,0.8894795477390289 -dairy-buffalo,BHS,0.0 -dairy-buffalo,BIH,0.0 -dairy-buffalo,BLR,0.0 -dairy-buffalo,BLZ,0.0 -dairy-buffalo,BOL,0.0 -dairy-buffalo,BRA,0.0 -dairy-buffalo,BRB,0.0 -dairy-buffalo,BRN,0.14139006286859512 -dairy-buffalo,BTN,0.0 -dairy-buffalo,BWA,0.0 -dairy-buffalo,CAF,0.0 -dairy-buffalo,CAN,0.0 -dairy-buffalo,CHE,0.0 -dairy-buffalo,CHL,0.0 -dairy-buffalo,CHN,0.2992595434188843 -dairy-buffalo,CIV,0.0 -dairy-buffalo,CMR,0.0 -dairy-buffalo,COD,0.0 -dairy-buffalo,COG,0.0 -dairy-buffalo,COL,0.0 -dairy-buffalo,COM,0.0 -dairy-buffalo,CPV,0.0 -dairy-buffalo,CRI,0.0 -dairy-buffalo,CUB,0.0 -dairy-buffalo,CYP,0.0 -dairy-buffalo,CZE,0.0 -dairy-buffalo,DEU,0.0 -dairy-buffalo,DJI,0.0 -dairy-buffalo,DNK,0.0 -dairy-buffalo,DOM,0.0 -dairy-buffalo,DZA,0.0 -dairy-buffalo,ECU,0.0 -dairy-buffalo,EGY,0.3171373214572668 -dairy-buffalo,ERI,0.0 -dairy-buffalo,ESP,0.0 -dairy-buffalo,EST,0.0 -dairy-buffalo,ETH,0.0 -dairy-buffalo,FIN,0.0 -dairy-buffalo,FJI,0.0 -dairy-buffalo,FRA,0.0 -dairy-buffalo,GAB,0.0 -dairy-buffalo,GBR,0.0 -dairy-buffalo,GEO,0.0 -dairy-buffalo,GHA,0.0 -dairy-buffalo,GIN,0.0 -dairy-buffalo,GMB,0.0 -dairy-buffalo,GNB,0.0 -dairy-buffalo,GNQ,0.0 -dairy-buffalo,GRC,0.34774744510650635 -dairy-buffalo,GRD,0.0 -dairy-buffalo,GTM,0.0 -dairy-buffalo,GUF,0.0 -dairy-buffalo,GUY,0.0 -dairy-buffalo,HND,0.0 -dairy-buffalo,HRV,0.0 -dairy-buffalo,HTI,0.0 -dairy-buffalo,HUN,0.0 -dairy-buffalo,IDN,0.16269265860319138 -dairy-buffalo,IND,0.35989967465866357 -dairy-buffalo,IRL,0.0 -dairy-buffalo,IRN,1.013802945613861 -dairy-buffalo,IRQ,0.2742725610733032 -dairy-buffalo,ISL,0.0 -dairy-buffalo,ISR,0.0 -dairy-buffalo,ITA,0.7516500949859619 -dairy-buffalo,JAM,0.0 -dairy-buffalo,JOR,0.0 -dairy-buffalo,JPN,0.0 -dairy-buffalo,KAZ,0.0 -dairy-buffalo,KEN,0.0 -dairy-buffalo,KGZ,0.0 -dairy-buffalo,KHM,0.0 -dairy-buffalo,KOR,0.0 -dairy-buffalo,LAO,0.0 -dairy-buffalo,LBN,0.0 -dairy-buffalo,LBR,0.0 -dairy-buffalo,LBY,0.0 -dairy-buffalo,LKA,0.3663536310195923 -dairy-buffalo,LSO,0.0 -dairy-buffalo,LTU,0.0 -dairy-buffalo,LUX,0.0 -dairy-buffalo,LVA,0.0 -dairy-buffalo,MAR,0.0 -dairy-buffalo,MDA,0.0 -dairy-buffalo,MDG,0.0 -dairy-buffalo,MEX,0.0 -dairy-buffalo,MKD,0.0 -dairy-buffalo,MLI,0.0 -dairy-buffalo,MLT,0.0 -dairy-buffalo,MMR,0.17905297130346298 -dairy-buffalo,MNE,0.0 -dairy-buffalo,MNG,0.0 -dairy-buffalo,MOZ,0.0 -dairy-buffalo,MRT,0.0 -dairy-buffalo,MUS,0.0 -dairy-buffalo,MWI,0.0 -dairy-buffalo,MYS,0.2809903919696808 -dairy-buffalo,NAM,0.0 -dairy-buffalo,NER,0.0 -dairy-buffalo,NGA,0.0 -dairy-buffalo,NIC,0.0 -dairy-buffalo,NLD,0.0 -dairy-buffalo,NOR,0.0 -dairy-buffalo,NPL,0.22075140662491322 -dairy-buffalo,NZL,0.0 -dairy-buffalo,OMN,0.0 -dairy-buffalo,PAK,0.848009467124939 -dairy-buffalo,PAN,0.0 -dairy-buffalo,PER,0.0 -dairy-buffalo,PHL,0.0 -dairy-buffalo,PNG,0.0 -dairy-buffalo,POL,0.0 -dairy-buffalo,PRI,0.0 -dairy-buffalo,PRT,0.0 -dairy-buffalo,PRY,0.0 -dairy-buffalo,PSE,0.0 -dairy-buffalo,ROU,0.0 -dairy-buffalo,RUS,0.0 -dairy-buffalo,RWA,0.0 -dairy-buffalo,SAU,0.0 -dairy-buffalo,SDN,0.0 -dairy-buffalo,SEN,0.0 -dairy-buffalo,SLB,0.0 -dairy-buffalo,SLE,0.0 -dairy-buffalo,SLV,0.0 -dairy-buffalo,SOM,0.0 -dairy-buffalo,SRB,0.0 -dairy-buffalo,SSD,0.0 -dairy-buffalo,STP,0.0 -dairy-buffalo,SUR,0.0 -dairy-buffalo,SVK,0.0 -dairy-buffalo,SVN,0.0 -dairy-buffalo,SWE,0.0 -dairy-buffalo,SWZ,0.0 -dairy-buffalo,SYR,0.471379891037941 -dairy-buffalo,TCD,0.0 -dairy-buffalo,TGO,0.0 -dairy-buffalo,THA,0.0 -dairy-buffalo,TJK,0.0 -dairy-buffalo,TKM,0.0 -dairy-buffalo,TLS,0.0 -dairy-buffalo,TTO,0.0 -dairy-buffalo,TUN,0.0 -dairy-buffalo,TUR,0.5339683294296265 -dairy-buffalo,TWN,0.0 -dairy-buffalo,TZA,0.0 -dairy-buffalo,UGA,0.0 -dairy-buffalo,UKR,0.0 -dairy-buffalo,URY,0.0 -dairy-buffalo,USA,0.0 -dairy-buffalo,UZB,0.0 -dairy-buffalo,VEN,0.0 -dairy-buffalo,VNM,0.15476837009191513 -dairy-buffalo,VUT,0.0 -dairy-buffalo,YEM,0.0 -dairy-buffalo,ZAF,0.0 -dairy-buffalo,ZMB,0.0 -dairy-buffalo,ZWE,0.0 -eggs,AFG,-0.13927175104618073 -eggs,AGO,-0.06172671169042587 -eggs,ALB,0.011954247951507568 -eggs,ARE,-0.002079929690808058 -eggs,ARG,-0.024128708988428116 -eggs,ARM,0.0014589187921956182 -eggs,ASM,0.0 -eggs,ATG,-0.05346987396478653 -eggs,AUS,-0.03890274092555046 -eggs,AUT,0.0011767512187361717 -eggs,AZE,-0.0010131524177268147 -eggs,BDI,-0.1374470591545105 -eggs,BEL,0.0 -eggs,BEN,-0.15669099986553192 -eggs,BFA,-0.1656169444322586 -eggs,BGD,-0.13400308787822723 -eggs,BGR,0.0 -eggs,BHS,-0.030595149844884872 -eggs,BIH,-0.0022013105917721987 -eggs,BLR,-0.003811200149357319 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+meat-sheep,MNG,-0.02277572639286518 +meat-sheep,MOZ,-0.09410440362989902 +meat-sheep,MRT,-0.048233019188046455 +meat-sheep,MUS,-0.04890628345310688 +meat-sheep,MWI,-0.04697384871542454 +meat-sheep,MYS,-0.09491143003106117 +meat-sheep,NAM,-0.08716513216495514 +meat-sheep,NER,-0.069356394931674 +meat-sheep,NGA,-0.023270415142178535 +meat-sheep,NIC,-0.0 +meat-sheep,NLD,0.013609923422336578 +meat-sheep,NOR,0.05332291964441538 +meat-sheep,NPL,-0.05352458171546459 +meat-sheep,NZL,0.0005488632596097887 +meat-sheep,OMN,-0.10465916991233826 +meat-sheep,PAK,-0.07066883333027363 +meat-sheep,PAN,-0.0 +meat-sheep,PER,-0.03324451670050621 +meat-sheep,PHL,-0.08635231107473373 +meat-sheep,PNG,-0.026137974113225937 +meat-sheep,POL,0.011744205374270678 +meat-sheep,PRI,-0.01051483117043972 +meat-sheep,PRT,0.098675437271595 +meat-sheep,PRY,-0.007636617810931057 +meat-sheep,PSE,-0.055522702634334564 +meat-sheep,ROU,-0.016659659333527088 +meat-sheep,RUS,0.012786585837602615 +meat-sheep,RWA,-0.038569577038288116 +meat-sheep,SAU,-0.07191868126392365 +meat-sheep,SDN,-0.05038566328585148 +meat-sheep,SEN,-0.07243901304900646 +meat-sheep,SLB,-0.0 +meat-sheep,SLE,-0.07532412931323051 +meat-sheep,SLV,-0.03697171760722995 +meat-sheep,SOM,-0.05844439938664436 +meat-sheep,SRB,0.08748101070523262 +meat-sheep,SSD,-0.040212699212133884 +meat-sheep,STP,-0.002143390476703644 +meat-sheep,SUR,0.01047318521887064 +meat-sheep,SVK,0.013529479503631592 +meat-sheep,SVN,0.08259506896138191 +meat-sheep,SWE,0.08849150314927101 +meat-sheep,SWZ,-0.09922288358211517 +meat-sheep,SYR,-0.09117881953716278 +meat-sheep,TCD,-0.07071537710726261 +meat-sheep,TGO,-0.06159967742860317 +meat-sheep,THA,-0.0667047593742609 +meat-sheep,TJK,-0.08428248763084412 +meat-sheep,TKM,-0.12523771077394485 +meat-sheep,TLS,-0.0782748181372881 +meat-sheep,TTO,0.0053625027649104595 +meat-sheep,TUN,-0.05145137198269367 +meat-sheep,TUR,-0.05173586402088404 +meat-sheep,TWN,-0.0 +meat-sheep,TZA,-0.06165468320250511 +meat-sheep,UGA,-0.09359623864293098 +meat-sheep,UKR,0.0233253741171211 +meat-sheep,URY,-0.003583780489861965 +meat-sheep,USA,0.005756152328103781 +meat-sheep,UZB,-0.07489606738090515 +meat-sheep,VEN,-0.04798725713044405 +meat-sheep,VNM,-0.08581174910068512 +meat-sheep,VUT,-0.0925622433423996 +meat-sheep,YEM,-0.055183399468660355 +meat-sheep,ZAF,-0.0121488724835217 +meat-sheep,ZMB,-0.050897425040602684 +meat-sheep,ZWE,-0.33774327486753464 diff --git a/data/curated/calibration/gbd-anchored/crop_cost.csv b/data/curated/calibration/gbd-anchored/crop_cost.csv index 07091668..cdf5f32d 100644 --- a/data/curated/calibration/gbd-anchored/crop_cost.csv +++ b/data/curated/calibration/gbd-anchored/crop_cost.csv @@ -1,5346 +1,5353 @@ crop,country,correction_bnusd_per_mha -alfalfa,AFG,-0.3750787377357483 -alfalfa,AGO,-0.2651873230934143 -alfalfa,ALB,0.0 -alfalfa,ARE,4.513212203979492 -alfalfa,ARG,-0.5347599685192108 -alfalfa,ARM,0.0 -alfalfa,ATG,0.0 -alfalfa,AUS,0.0 -alfalfa,AUT,-0.755619078874588 -alfalfa,AZE,0.8579510450363159 -alfalfa,BDI,-0.8987658023834229 -alfalfa,BEL,-0.28935573995113373 -alfalfa,BEN,0.8655805587768555 -alfalfa,BFA,0.0 -alfalfa,BGD,-0.7333669364452362 -alfalfa,BGR,-0.033132417127490044 -alfalfa,BHS,-0.6666980385780334 -alfalfa,BIH,0.16280731558799744 -alfalfa,BLR,-0.28629985451698303 -alfalfa,BLZ,-0.5492207109928131 -alfalfa,BOL,-0.22297222912311554 -alfalfa,BRA,-0.4606279283761978 -alfalfa,BRB,2.77047598361969 -alfalfa,BRN,1.4280236959457397 -alfalfa,BTN,0.08275410532951355 -alfalfa,BWA,0.0 -alfalfa,CAF,-0.6585278511047363 -alfalfa,CAN,-0.11497456952929497 -alfalfa,CHE,0.09321406949311495 -alfalfa,CHL,-0.6260356903076172 -alfalfa,CHN,0.5708749294281006 +alfalfa,AFG,-0.3613618314266205 +alfalfa,AGO,-0.9381504952907562 +alfalfa,ALB,-0.5532752722501755 +alfalfa,ARE,-0.18636378645896912 +alfalfa,ARG,-0.4758569896221161 +alfalfa,ARM,-0.0 +alfalfa,ATG,-7.5 +alfalfa,AUS,0.02438315376639366 +alfalfa,AUT,-0.8089405298233032 +alfalfa,AZE,0.33599771559238434 +alfalfa,BDI,-0.8992443978786469 +alfalfa,BEL,0.045041752979159355 +alfalfa,BEN,0.38562148809432983 +alfalfa,BFA,-0.0 +alfalfa,BGD,-0.7334521114826202 +alfalfa,BGR,-0.25662170350551605 +alfalfa,BHS,-0.6647240519523621 +alfalfa,BIH,3.645371161401272 +alfalfa,BLR,-2.7995163053274155 +alfalfa,BLZ,-0.010105629451572895 +alfalfa,BOL,-0.33576732873916626 +alfalfa,BRA,-0.656684398651123 +alfalfa,BRB,1.8175461292266846 +alfalfa,BRN,-0.8346583247184753 +alfalfa,BTN,-0.2557598948478699 +alfalfa,BWA,-7.5 +alfalfa,CAF,-0.6662335991859436 +alfalfa,CAN,-0.4989238977432251 +alfalfa,CHE,-0.14492646604776382 +alfalfa,CHL,-0.868007481098175 +alfalfa,CHN,0.10536020249128342 alfalfa,CIV,7.5 -alfalfa,CMR,1.7987051606178284 -alfalfa,COD,-0.1673155128955841 -alfalfa,COG,-0.9112309217453003 -alfalfa,COL,-0.9038967788219452 -alfalfa,COM,0.7247816920280457 -alfalfa,CPV,-0.3923039734363556 -alfalfa,CRI,-0.21360260248184204 -alfalfa,CUB,-0.28110775351524353 -alfalfa,CYP,-7.5 -alfalfa,CZE,-0.5772684216499329 -alfalfa,DEU,-0.0678469343110919 -alfalfa,DJI,1.6652567386627197 -alfalfa,DNK,7.5 -alfalfa,DOM,-0.8691022992134094 -alfalfa,DZA,-0.07842585444450378 -alfalfa,ECU,-0.12939461693167686 -alfalfa,EGY,-0.24773885309696198 -alfalfa,ERI,-0.8744806945323944 -alfalfa,ESP,-1.0377808809280396 -alfalfa,EST,-0.4961422383785248 -alfalfa,ETH,-0.8274461328983307 -alfalfa,FIN,0.0 -alfalfa,FJI,-0.0291437734849751 -alfalfa,FRA,-0.3310399800539017 -alfalfa,GAB,-0.870643675327301 -alfalfa,GBR,0.15797654539346695 -alfalfa,GEO,-0.583319365978241 -alfalfa,GHA,7.5 -alfalfa,GIN,-0.387450709939003 -alfalfa,GMB,-0.242458738386631 -alfalfa,GNB,0.0 -alfalfa,GNQ,-0.7589189112186432 -alfalfa,GRC,-0.6836957335472107 -alfalfa,GRD,0.5122256278991699 -alfalfa,GTM,-0.5226822793483734 -alfalfa,GUF,-2.706025242805481 -alfalfa,GUY,-0.07720011845231056 -alfalfa,HND,-0.34651389718055725 -alfalfa,HRV,-3.0907102823257446 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-alfalfa,UZB,-1.3574955463409424 -alfalfa,VEN,-0.27461254596710205 -alfalfa,VNM,0.21912385523319244 -alfalfa,VUT,-0.48392657935619354 -alfalfa,YEM,-0.6435136795043945 -alfalfa,ZAF,-0.45210833847522736 -alfalfa,ZMB,-0.3911762535572052 -alfalfa,ZWE,0.0 -apple,AFG,0.0 -apple,ALB,0.0 -apple,ARG,0.0 -apple,ARM,0.0 -apple,AUS,0.0 -apple,AUT,0.0 -apple,AZE,0.0 -apple,BEL,0.0 -apple,BGR,0.0 -apple,BIH,0.0 -apple,BLR,0.0 -apple,BLZ,0.0 -apple,BOL,0.0 -apple,BRA,0.0 -apple,BTN,0.0 -apple,BWA,0.0 -apple,CAN,0.0 -apple,CHE,0.0 -apple,CHL,0.0 -apple,CHN,0.0 -apple,COL,0.0 -apple,CZE,0.0 -apple,DEU,0.0 -apple,DNK,0.0 -apple,DZA,0.0 -apple,ECU,0.0 -apple,EGY,0.0 -apple,ESP,0.0 -apple,EST,0.0 -apple,ETH,0.0 -apple,FIN,0.0 -apple,FRA,0.0 -apple,GBR,0.0 -apple,GEO,0.0 -apple,GRC,0.0 -apple,GTM,0.0 -apple,HND,0.0 -apple,HRV,0.0 -apple,HUN,0.0 -apple,IND,0.0 -apple,IRL,0.0 -apple,IRN,0.0 -apple,IRQ,0.0 -apple,ISR,0.0 -apple,ITA,0.0 -apple,JOR,0.0 -apple,JPN,0.0 -apple,KAZ,0.0 -apple,KEN,0.0 -apple,KGZ,0.0 -apple,KOR,0.0 -apple,LAO,0.0 -apple,LBN,0.0 -apple,LBY,0.0 -apple,LSO,0.0 -apple,LTU,0.0 -apple,LUX,0.0 -apple,LVA,0.0 -apple,MAR,0.0 -apple,MDA,0.0 -apple,MDG,-1.9327446222305298 -apple,MEX,0.0 -apple,MKD,0.0 -apple,MMR,0.0 -apple,MNE,0.0 -apple,MNG,0.0 -apple,MOZ,0.0 -apple,NIC,0.0 -apple,NLD,0.0 -apple,NOR,0.0 -apple,NPL,0.0 -apple,NZL,0.0 -apple,PAK,0.0 -apple,PER,0.0 -apple,POL,0.0 -apple,PRT,0.0 -apple,PRY,0.0 -apple,PSE,0.0 -apple,ROU,0.0 -apple,RUS,0.0 +alfalfa,CMR,0.4638580083847046 +alfalfa,COD,-0.10928117483854294 +alfalfa,COG,-0.09945236518979073 +alfalfa,COL,-0.1985142081975937 +alfalfa,COM,0.313687726855278 +alfalfa,CPV,-7.5 +alfalfa,CRI,-0.22407843172550201 +alfalfa,CUB,-0.2862354665994644 +alfalfa,CYP,2.298451542854309 +alfalfa,CZE,-0.5457680821418762 +alfalfa,DEU,-0.19640730321407318 +alfalfa,DJI,-0.835690438747406 +alfalfa,DNK,6.0882463455200195 +alfalfa,DOM,-1.1007803976535797 +alfalfa,DZA,0.14527757093310356 +alfalfa,ECU,-0.07392867282032967 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-wheat,SSD,3.5920525640249252 -wheat,SVK,-0.23884376138448715 -wheat,SVN,-0.0217582571785897 -wheat,SWE,-0.6687245070934296 -wheat,SWZ,-0.43187111616134644 -wheat,SYR,-0.35154592990875244 -wheat,TCD,-0.5343460440635681 +wheat,NLD,-1.8853646516799927 +wheat,NOR,-1.2634109854698181 +wheat,NPL,-0.3843866139650345 +wheat,NZL,-1.3733990788459778 +wheat,OMN,-2.367879092693329 +wheat,PAK,-0.4402499198913574 +wheat,PER,-0.3972070813179016 +wheat,POL,-0.4910983741283417 +wheat,PRT,3.625117279589176 +wheat,PRY,-0.15593130886554718 +wheat,PSE,-0.6957190930843353 +wheat,ROU,-0.3528255224227905 +wheat,RUS,-0.2881578803062439 +wheat,RWA,-0.4157772809267044 +wheat,SAU,-2.201143980026245 +wheat,SDN,-0.6401222348213196 +wheat,SLV,-0.0 +wheat,SOM,-0.700070858001709 +wheat,SRB,-0.19848661124706268 +wheat,SSD,3.5394650250673294 +wheat,SVK,-0.3735336810350418 +wheat,SVN,-0.35965174436569214 +wheat,SWE,-0.9167700707912445 +wheat,SWZ,-0.4809947907924652 +wheat,SYR,-0.4681524634361267 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-white-potato,CIV,-7.043319463729858 -white-potato,CMR,-5.917961835861206 -white-potato,COD,-6.6615283489227295 -white-potato,COG,-6.1582489013671875 -white-potato,COL,-2.460464835166931 -white-potato,COM,-6.870412349700928 -white-potato,CPV,-7.5 +white-potato,CHL,-5.076667785644531 +white-potato,CHN,-0.16349898278713226 +white-potato,CIV,-7.068480491638184 +white-potato,CMR,-6.015861749649048 +white-potato,COD,-6.667510986328125 +white-potato,COG,-6.153130292892456 +white-potato,COL,-2.7065480947494507 +white-potato,COM,-7.182870626449585 +white-potato,CPV,-6.651533603668213 white-potato,CRI,-7.5 -white-potato,CUB,-4.7171337604522705 -white-potato,CYP,-7.273747205734253 -white-potato,CZE,-0.39465928077697754 -white-potato,DEU,0.0643875002861023 -white-potato,DNK,-2.6065115928649902 +white-potato,CUB,-4.543330192565918 +white-potato,CYP,-4.8008973598480225 +white-potato,CZE,-0.1767502874135971 +white-potato,DEU,0.3590337112545967 +white-potato,DNK,-2.760642886161804 white-potato,DOM,-7.5 -white-potato,DZA,-4.705379009246826 -white-potato,ECU,-1.3077362775802612 -white-potato,EGY,-3.288508653640747 -white-potato,ERI,-7.13643741607666 -white-potato,ESP,-6.394805431365967 -white-potato,EST,0.7056463360786438 -white-potato,ETH,0.10911436378955841 -white-potato,FIN,-0.7054619193077087 +white-potato,DZA,-5.447375059127808 +white-potato,ECU,-1.6488671898841858 +white-potato,EGY,-2.450685739517212 +white-potato,ERI,-7.137639999389648 +white-potato,ESP,-5.260821104049683 +white-potato,EST,0.8071079850196838 +white-potato,ETH,0.5625659227371216 +white-potato,FIN,-0.6591880619525909 white-potato,FRA,-7.5 -white-potato,GAB,-7.0301525592803955 -white-potato,GBR,-3.9957441091537476 -white-potato,GEO,-0.5431082993745804 -white-potato,GHA,-7.04927659034729 -white-potato,GIN,-6.127397060394287 -white-potato,GMB,-1.4287139177322388 -white-potato,GNB,-6.587334632873535 -white-potato,GNQ,-6.750863790512085 +white-potato,GAB,-7.02944278717041 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-white-potato,SRB,-0.8770628869533539 -white-potato,SSD,-4.87265419960022 -white-potato,SUR,-3.8383898735046387 -white-potato,SVK,-3.157607913017273 -white-potato,SVN,-2.6154524087905884 -white-potato,SWE,-4.749576568603516 -white-potato,SWZ,-7.002017021179199 -white-potato,SYR,-3.658203363418579 -white-potato,TCD,-6.417938232421875 -white-potato,TGO,-6.82444429397583 -white-potato,THA,-1.943368136882782 -white-potato,TJK,-1.807421088218689 -white-potato,TKM,-4.701775074005127 -white-potato,TLS,-0.4336884915828705 -white-potato,TTO,-5.9028894901275635 -white-potato,TUN,-3.0832247734069824 -white-potato,TUR,-1.3715746998786926 -white-potato,TWN,-2.6386871337890625 -white-potato,TZA,-0.2126503884792328 -white-potato,UGA,-6.692795991897583 -white-potato,UKR,0.09153179824352264 +white-potato,SDN,-5.008236646652222 +white-potato,SEN,-5.62498927116394 +white-potato,SLB,-4.641535997390747 +white-potato,SLE,0.7965755462646484 +white-potato,SLV,0.9726858139038086 +white-potato,SOM,-5.723247051239014 +white-potato,SRB,1.4379744827747345 +white-potato,SSD,-7.5 +white-potato,SUR,-7.5 +white-potato,SVK,-1.6193366423249245 +white-potato,SVN,-2.6869579553604126 +white-potato,SWE,-4.817352771759033 +white-potato,SWZ,-6.997324466705322 +white-potato,SYR,-3.6495532989501953 +white-potato,TCD,-6.66522479057312 +white-potato,TGO,-6.855196714401245 +white-potato,THA,-2.274385929107666 +white-potato,TJK,-1.6017606258392334 +white-potato,TKM,-5.116387844085693 +white-potato,TLS,-0.47124533355236053 +white-potato,TTO,-6.704572677612305 +white-potato,TUN,-1.308105707168579 +white-potato,TUR,-1.3780488967895508 +white-potato,TWN,-2.6755971908569336 +white-potato,TZA,-0.15896368026733398 +white-potato,UGA,-6.633254766464233 +white-potato,UKR,0.5078345239162445 white-potato,URY,-7.5 -white-potato,USA,1.0335465669631958 -white-potato,UZB,-3.4073362350463867 -white-potato,VEN,-3.917459487915039 -white-potato,VNM,-4.783044099807739 -white-potato,YEM,-7.499095439910889 -white-potato,ZAF,-5.644451379776001 +white-potato,USA,0.7869330644607544 +white-potato,UZB,-3.1113035678863525 +white-potato,VEN,-7.5 +white-potato,VNM,-4.910987377166748 +white-potato,YEM,-5.8191752433776855 +white-potato,ZAF,-3.93700909614563 white-potato,ZMB,-7.5 white-potato,ZWE,-7.5 -yam,AFG,0.0 -yam,AGO,-2.71264386177063 -yam,ARG,7.5 -yam,ATG,-4.396996021270752 -yam,BDI,-4.46341609954834 -yam,BEN,-1.1236973404884338 -yam,BFA,-2.659050464630127 -yam,BIH,0.0 -yam,BLZ,-1.2888553142547607 -yam,BOL,-5.130490303039551 -yam,BRA,-2.8824214935302734 +yam,AFG,-0.0 +yam,AGO,-4.3795061111450195 +yam,ARG,2.721688985824585 +yam,ATG,-4.508057594299316 +yam,BDI,-4.5215067863464355 +yam,BEN,-1.3934463858604431 +yam,BFA,-2.313081979751587 +yam,BIH,3.75 +yam,BLZ,-1.5160371661186218 +yam,BOL,-5.210255146026611 +yam,BRA,-2.879700779914856 yam,BRB,-7.5 -yam,BRN,-5.194641351699829 -yam,BTN,-4.156153202056885 -yam,BWA,-3.4835290908813477 -yam,CAF,-3.970792531967163 -yam,CHL,0.0 -yam,CHN,-0.30300410091876984 -yam,CIV,-0.6973100304603577 -yam,CMR,-2.890680193901062 -yam,COD,-4.217174291610718 -yam,COG,-4.213520526885986 -yam,COL,-0.541134238243103 -yam,COM,-4.306869983673096 +yam,BRN,-5.240689039230347 +yam,BTN,-2.9088377952575684 +yam,BWA,-3.6050477027893066 +yam,CAF,-4.071576118469238 +yam,CHN,-0.7571600675582886 +yam,CIV,-0.7280837893486023 +yam,CMR,-3.7071540355682373 +yam,COD,-4.309177398681641 +yam,COG,-4.303754806518555 +yam,COL,-1.327725887298584 +yam,COM,-4.371114492416382 yam,CRI,-7.5 -yam,CUB,-2.359723925590515 -yam,DOM,-1.9253771305084229 -yam,ECU,-5.317235469818115 -yam,EGY,-1.964463472366333 -yam,ERI,-5.59840989112854 -yam,ETH,-1.4126824140548706 +yam,CUB,-2.746282696723938 +yam,DOM,-1.9059787392616272 +yam,ECU,-5.174973964691162 +yam,EGY,0.33129335939884186 +yam,ERI,-5.6153175830841064 +yam,ETH,-1.8165869116783142 yam,FJI,-7.5 -yam,GAB,-4.192934989929199 -yam,GHA,-1.1651211977005005 -yam,GIN,-2.261919379234314 -yam,GMB,0.0 -yam,GNB,-4.171942234039307 -yam,GNQ,-4.465688228607178 +yam,GAB,-4.273389577865601 +yam,GHA,-1.3959007859230042 +yam,GIN,-2.1967345476150513 +yam,GMB,-7.5 +yam,GNB,-4.171043395996094 +yam,GNQ,-4.524336338043213 yam,GRD,-7.5 -yam,GTM,7.5 -yam,GUF,0.0 +yam,GTM,1.9312936067581177 +yam,GUF,-0.0 yam,GUY,-7.5 -yam,HND,-4.0838319063186646 -yam,HRV,0.0 -yam,HTI,-5.307753562927246 -yam,IDN,-4.6345484256744385 -yam,IND,-4.509192943572998 -yam,IRN,0.0 -yam,ISR,3.181663453578949 +yam,HND,-4.330725193023682 +yam,HRV,-0.0 +yam,HTI,-5.377715826034546 +yam,IDN,-4.672621250152588 +yam,IND,5.215112209320068 +yam,IRN,-0.0 +yam,ISR,3.2410873770713806 yam,JAM,-7.5 yam,JOR,7.5 yam,JPN,-7.5 -yam,KEN,-1.518750011920929 -yam,KHM,-2.45957350730896 -yam,LAO,2.1110036373138428 -yam,LBN,-2.3234525322914124 -yam,LBR,-3.763749837875366 -yam,LKA,-3.0244040489196777 -yam,LSO,0.0 -yam,MDG,-4.0314555168151855 -yam,MEX,2.5281636714935303 -yam,MLI,-6.964144945144653 -yam,MMR,1.947170078754425 -yam,MNE,0.0 -yam,MOZ,-3.857971429824829 -yam,MRT,-7.5 -yam,MUS,-3.4608681201934814 -yam,MWI,7.5 -yam,MYS,-2.66167676448822 -yam,NAM,-2.9809781312942505 -yam,NER,-5.249086618423462 -yam,NGA,-2.7895864248275757 -yam,NIC,-1.962601125240326 -yam,NPL,-6.539735317230225 -yam,PAK,1.2216424942016602 -yam,PAN,-3.1514991521835327 -yam,PER,-3.699566960334778 -yam,PHL,-1.6030202507972717 -yam,PNG,-2.5849483013153076 -yam,PRI,-7.476719617843628 +yam,KEN,-1.4882037043571472 +yam,KHM,-2.6322381496429443 +yam,LAO,0.9493604302406311 +yam,LBN,-4.759747624397278 +yam,LBR,-3.783653497695923 +yam,LKA,-3.0860074758529663 +yam,LSO,-0.0 +yam,MDG,-4.15936541557312 +yam,MEX,2.388148307800293 +yam,MLI,-5.260771751403809 +yam,MMR,-0.9187805652618408 +yam,MNE,-0.0 +yam,MOZ,-4.720427751541138 +yam,MRT,-4.346336841583252 +yam,MUS,-3.5727826356887817 +yam,MWI,1.796617031097412 +yam,MYS,-2.7936019897460938 +yam,NAM,-3.4631645679473877 +yam,NER,-5.156688213348389 +yam,NGA,-3.9387731552124023 +yam,NIC,2.688100576400757 +yam,NPL,-5.065113067626953 +yam,PAK,-0.0 +yam,PAN,-3.2264866828918457 +yam,PER,-3.998135209083557 +yam,PHL,-1.6469984650611877 +yam,PNG,-3.0244327783584595 +yam,PRI,-7.5 yam,PRY,7.5 -yam,PSE,-1.2889548242092133 -yam,RWA,-0.815086305141449 -yam,SAU,0.0 -yam,SDN,-5.722858905792236 -yam,SEN,-2.11346173286438 -yam,SLB,-1.7412835955619812 -yam,SLE,-5.370323419570923 -yam,SLV,2.908381938934326 -yam,SOM,0.0 -yam,SSD,-5.157663822174072 -yam,STP,-4.726482629776001 -yam,SUR,-2.0496089458465576 -yam,SWZ,-3.945995330810547 -yam,SYR,0.0 -yam,TCD,-4.010426998138428 -yam,TGO,-1.077474057674408 -yam,THA,-1.807403028011322 -yam,TLS,-5.455284118652344 +yam,PSE,-1.4629396200180054 +yam,RWA,-0.8948467671871185 +yam,SAU,-0.0 +yam,SDN,-5.548225402832031 +yam,SEN,-0.9069963693618774 +yam,SLB,-2.2544283866882324 +yam,SLE,-5.428421974182129 +yam,SLV,2.689927101135254 +yam,SOM,7.5 +yam,SSD,-5.277986526489258 +yam,STP,-4.7317304611206055 +yam,SUR,-2.4590797424316406 +yam,SWZ,-4.03550910949707 +yam,SYR,-0.0 +yam,TCD,-4.677451848983765 +yam,TGO,-1.02513986825943 +yam,THA,-2.1230387687683105 +yam,TLS,-5.495773553848267 yam,TTO,-7.5 -yam,TWN,-2.3733160495758057 -yam,TZA,-4.54769492149353 -yam,UGA,-3.8769387006759644 -yam,URY,0.0 -yam,USA,6.12684440612793 -yam,VEN,-2.043738603591919 -yam,VNM,-3.632774829864502 -yam,VUT,-3.0644670724868774 -yam,ZAF,-4.348750352859497 -yam,ZMB,7.5 -yam,ZWE,-3.2570632696151733 +yam,TWN,-2.740014672279358 +yam,TZA,-4.686638355255127 +yam,UGA,-4.014009714126587 +yam,URY,-0.0 +yam,USA,7.5 +yam,VEN,-2.4075191020965576 +yam,VNM,-2.437577247619629 +yam,VUT,-3.4503121376037598 +yam,ZAF,-4.382262229919434 +yam,ZMB,-3.4753758907318115 +yam,ZWE,-3.3793407678604126 diff --git a/data/curated/calibration/gbd-anchored/deviation_penalty.yaml b/data/curated/calibration/gbd-anchored/deviation_penalty.yaml index 0142c962..1f0f9e87 100644 --- a/data/curated/calibration/gbd-anchored/deviation_penalty.yaml +++ b/data/curated/calibration/gbd-anchored/deviation_penalty.yaml @@ -14,9 +14,9 @@ components: - grassland - feed l1_costs: - cropland: 2.40016323547014 - grassland: 0.16315900048299714 - feed: 0.09429247220349436 -iterations: 3 + cropland: 1.6191595998981074 + grassland: 0.24020086409748675 + feed: 0.05576680765925167 +iterations: 4 converged: true -final_residual_log_inf: 0.01443185103258953 +final_residual_log_inf: 0.010390503539944175 diff --git a/data/curated/calibration/gbd-anchored/exogenous_feed.csv b/data/curated/calibration/gbd-anchored/exogenous_feed.csv index 89b7d00f..6d43d8d1 100644 --- a/data/curated/calibration/gbd-anchored/exogenous_feed.csv +++ b/data/curated/calibration/gbd-anchored/exogenous_feed.csv @@ -2,104 +2,105 @@ # # SPDX-License-Identifier: CC-BY-4.0 country,monogastric_protein_mt_dm,ruminant_protein_mt_dm,ruminant_roughage_mt_dm -AFG,0.0,0.0,6.454943 -AGO,0.0,0.0,0.935912 -ARE,0.0,0.0,1.41976 -ARM,0.0,0.0,0.921952 +AFG,0.0,0.409843,6.396201 +AGO,0.0,0.0,0.921706 +ARE,0.0,0.0,1.420038 +ARM,0.0,0.0,0.922057 ATG,0.0,0.0,0.001203 -AZE,0.0,0.0,1.66954 -BFA,0.0,0.0,2.427717 -BGD,0.0,0.0,9.224991 +AZE,0.0,0.0,1.662357 +BFA,0.0,0.0,2.424819 +BGD,0.0,0.0,9.323274 BHS,0.0,0.0,0.000241 -BOL,0.0,0.0,3.061838 -BRA,0.0,0.0,4.291774 +BOL,0.0,0.0,3.079534 +BRA,0.0,0.0,3.789062 BRB,0.0,0.0,0.001186 -BRN,0.0,0.0,0.098264 -BTN,0.0,0.0,0.032692 -BWA,0.0,0.0,0.803647 -CAF,0.0,0.0,3.145706 -CHL,0.0,0.0,1.174771 -CMR,0.0,0.0,1.961326 -COG,0.0,0.0,0.101832 -COL,0.0,0.0,4.967146 +BRN,0.031226,0.000603,0.098271 +BTN,0.0,0.0,0.032205 +BWA,0.0,0.0,0.801548 +CAF,0.0,0.0,3.139111 +CHL,0.0,0.0,1.159961 +CHN,2.78031,0.0,0.0 +CMR,0.0,0.0,2.066831 +COG,0.0,0.0,0.101918 +COL,0.0,0.0,4.970007 COM,0.0,0.0,0.023564 CPV,0.0,0.0,0.014816 -CRI,0.0,0.0,0.475468 -CUB,0.0,0.0,0.474311 +CRI,0.0,0.0,0.477516 +CUB,0.0,0.0,0.474619 CYP,0.0,0.0,0.026106 DJI,0.0,0.0,0.253119 -DOM,0.0,0.0,0.732578 -DZA,0.0,0.0,7.00384 -ECU,0.0,0.0,0.999379 -EGY,0.0,0.0,7.622975 -ERI,0.0,0.0,0.647038 -ETH,0.0,0.0,3.538134 -GEO,0.0,0.0,0.291168 -GNB,0.0,0.0,0.082517 +DOM,0.0,0.0,0.733529 +DZA,0.0,0.0,6.986687 +ECU,0.0,0.0,0.999478 +EGY,0.0,0.0,7.620932 +ERI,0.0,0.0,0.647088 +ETH,0.0,0.0,3.690289 +GEO,0.0,0.0,0.295198 +GNB,0.0,0.0,0.082731 GRD,0.0,0.0,0.003329 -GTM,0.0,0.0,0.882024 -HND,0.0,0.0,0.328047 -HTI,0.0,0.0,0.56707 -IDN,5.019596,0.0,0.0 -IND,0.0,0.0,289.459564 -IRL,0.0,0.0,0.334866 +GTM,0.0,0.0,0.901076 +HND,0.0,0.0,0.341193 +HTI,0.0,0.0,0.566936 +IDN,5.755511,0.0,0.0 +IND,0.0,0.0,289.03186 +IRL,0.0,0.0,0.339502 ISL,0.0,0.0,0.030581 -ISR,0.0,0.0,1.114115 +ISR,0.0,0.0,1.114146 JAM,0.0,0.0,0.181489 -JOR,0.0,0.0,0.909527 -JPN,3.636718,1.221458,1.321784 -KEN,0.0,0.0,8.557251 -KHM,0.027266,0.208704,0.0 -KOR,2.91681,0.374992,2.65805 -LAO,0.0,0.055929,0.0 +JOR,0.0,0.0,0.909003 +JPN,3.158886,1.139232,1.328452 +KEN,0.0,0.0,8.594836 +KHM,0.0,0.135893,0.0 +KOR,2.760149,0.301891,2.659726 +LAO,0.0,0.06121,0.0 LBN,0.0,0.0,0.255603 -LBY,0.0,0.0,0.793363 -LSO,0.0,0.0,0.093721 -MAR,0.0,0.0,5.567743 -MEX,0.0,0.0,2.575333 +LBY,0.0,0.0,0.793866 +LSO,0.0,0.0,0.092898 +MAR,0.0,0.0,5.582377 +MEX,0.0,0.0,2.590309 MLT,0.0,0.0,0.002164 -MNE,0.0,0.0,0.002303 -MNG,0.0,0.0,15.397603 -MRT,0.0,0.0,2.106061 -MYS,0.0,0.0,0.202319 -NAM,0.0,0.0,0.693314 -NER,0.0,0.0,1.711404 -NIC,0.0,0.0,1.291721 -NLD,0.0,0.0,0.117066 -NPL,0.0,0.0,15.855499 -NZL,0.0,0.488506,0.0 -OMN,0.0,0.0,1.601173 -PAK,0.0,4.235036,166.856613 -PAN,0.0,0.0,0.335518 -PER,0.0,0.0,0.826841 -PHL,1.438208,0.245323,0.0 +MMR,0.0,0.340268,0.0 +MNE,0.0,0.0,0.002311 +MNG,0.0,0.0,15.401712 +MRT,0.0,0.0,2.102427 +MYS,0.955496,0.0,0.234748 +NAM,0.0,0.0,0.700566 +NER,0.0,0.0,1.710052 +NIC,0.0,0.0,1.29114 +NLD,0.0,0.0,0.127665 +NPL,0.0,0.0,15.811893 +NZL,0.0,0.532795,0.0 +OMN,0.0,0.0,1.601246 +PAK,0.0,0.0,166.691833 +PAN,0.0,0.0,0.335598 +PER,0.0,0.0,0.808195 +PHL,1.881675,0.0,0.0 PRI,0.0,0.0,0.093587 -PRY,0.0,0.0,0.157387 -PSE,0.0,0.0,0.253417 -RWA,0.0,0.0,0.379823 -SAU,0.0,0.0,3.206031 -SDN,0.0,0.0,11.694079 -SEN,0.0,0.0,0.880737 -SOM,0.0,0.0,5.319843 -SSD,0.0,0.0,3.617177 +PRY,0.0,0.0,0.130072 +PSE,0.0,0.0,0.253437 +RWA,0.0,0.0,0.379596 +SAU,0.0,0.0,3.205631 +SDN,0.0,0.0,11.691299 +SEN,0.0,0.0,0.879997 +SOM,0.0,0.0,5.321395 +SSD,0.0,0.0,3.618346 STP,0.0,0.0,0.002251 -SWZ,0.0,0.0,0.169087 -SYR,0.0,0.0,1.271971 -TCD,0.0,0.0,25.861656 -THA,1.300704,0.019779,0.0 -TJK,0.0,0.0,0.247159 -TKM,0.0,0.0,0.695733 -TLS,0.003341,0.01035,0.066253 +SWZ,0.0,0.0,0.169131 +SYR,0.0,0.0,1.266636 +TCD,0.0,0.0,25.904709 +THA,0.537294,0.0,0.0 +TJK,0.0,0.18765,0.245753 +TKM,0.0,0.0,0.692459 +TLS,0.0,0.0,0.067068 TTO,0.0,0.0,0.00687 -TUN,0.0,0.0,1.242338 -TUR,0.0,0.0,1.668762 -TWN,0.219836,0.00074,0.0 -TZA,0.0,0.0,9.636807 -UGA,0.0,0.0,3.555149 -URY,0.0,0.0,2.438578 -VEN,0.0,0.0,3.763246 -VNM,1.433684,0.832676,0.0 -YEM,0.0,0.0,3.183534 -ZAF,0.0,0.0,5.482439 -ZMB,0.0,0.0,2.941376 -ZWE,0.0,0.0,10.748847 +TUN,0.0,0.0,1.232322 +TUR,0.0,0.0,1.617232 +TWN,1.266767,0.00074,0.0 +TZA,0.0,0.0,9.70183 +UGA,0.0,0.0,3.548997 +URY,0.0,0.0,2.438507 +VEN,0.0,0.0,3.760523 +YEM,0.0,0.0,3.182977 +ZAF,0.0,0.0,5.453259 +ZMB,0.0,0.0,2.947526 +ZWE,0.0,0.0,10.745438 diff --git a/data/curated/calibration/gbd-anchored/exogenous_forage.csv b/data/curated/calibration/gbd-anchored/exogenous_forage.csv index cb7c5f94..b6dfc491 100644 --- a/data/curated/calibration/gbd-anchored/exogenous_forage.csv +++ b/data/curated/calibration/gbd-anchored/exogenous_forage.csv @@ -5,7 +5,7 @@ country,exogenous_forage_mt_dm AFG,0.0 AGO,0.0 ALB,0.0 -ARE,1.890263 +ARE,1.955591 ARG,0.0 ARM,0.0 ATG,0.0 @@ -16,7 +16,7 @@ BDI,0.0 BEL,0.0 BEN,0.0 BFA,0.0 -BGD,17.304365 +BGD,17.314585 BGR,0.0 BHS,0.0 BIH,0.0 @@ -25,7 +25,7 @@ BLZ,0.0 BOL,0.0 BRA,0.0 BRB,0.0 -BRN,0.0 +BRN,0.073643 BTN,0.0 BWA,0.0 CAF,0.0 @@ -45,7 +45,7 @@ CUB,0.0 CYP,0.0 CZE,0.0 DEU,0.0 -DJI,0.0 +DJI,0.465381 DNK,0.0 DOM,0.0 DZA,0.0 @@ -76,7 +76,7 @@ HRV,0.0 HTI,0.0 HUN,0.0 IDN,0.0 -IND,148.093414 +IND,150.561417 IRL,0.0 IRN,0.0 IRQ,0.0 @@ -90,7 +90,7 @@ KAZ,0.0 KEN,0.0 KGZ,0.0 KHM,0.0 -KOR,5.239663 +KOR,0.0 LAO,0.0 LBN,0.0 LBR,0.0 @@ -107,7 +107,7 @@ MEX,0.0 MKD,0.0 MLI,0.0 MLT,0.0 -MMR,8.617109 +MMR,8.135522 MNE,0.0 MNG,0.0 MOZ,0.0 @@ -121,10 +121,10 @@ NGA,0.0 NIC,0.0 NLD,0.0 NOR,0.0 -NPL,2.597535 +NPL,2.535496 NZL,0.0 -OMN,1.69909 -PAK,70.964592 +OMN,1.702738 +PAK,71.103645 PAN,0.0 PER,0.0 PHL,0.0 @@ -156,19 +156,19 @@ SYR,0.0 TCD,0.0 TGO,0.0 THA,0.0 -TJK,3.302411 -TKM,9.849627 +TJK,3.664683 +TKM,0.932468 TLS,0.0 TTO,0.0 TUN,0.0 -TUR,0.0 +TUR,15.814188 TWN,0.0 TZA,0.0 UGA,0.0 UKR,0.0 URY,0.0 USA,0.0 -UZB,15.043672 +UZB,14.991374 VEN,0.0 VNM,0.0 VUT,0.0 diff --git a/data/curated/calibration/gbd-anchored/fodder_conversion.csv b/data/curated/calibration/gbd-anchored/fodder_conversion.csv index 1fd05b76..f2f3ce92 100644 --- a/data/curated/calibration/gbd-anchored/fodder_conversion.csv +++ b/data/curated/calibration/gbd-anchored/fodder_conversion.csv @@ -53,7 +53,7 @@ ECU,1.0 EGY,1.0 ERI,1.0 ESP,1.0 -EST,0.475502 +EST,0.475451 ETH,1.0 FIN,1.0 FJI,1.0 @@ -74,7 +74,7 @@ GUY,1.0 HND,1.0 HRV,1.0 HTI,1.0 -HUN,0.592926 +HUN,0.594011 IDN,1.0 IND,1.0 IRL,1.0 @@ -85,7 +85,7 @@ ISR,1.0 ITA,1.0 JAM,1.0 JOR,1.0 -JPN,0.763731 +JPN,0.768006 KAZ,1.0 KEN,1.0 KGZ,1.0 @@ -99,9 +99,9 @@ LKA,1.0 LSO,1.0 LTU,1.0 LUX,1.0 -LVA,0.294322 +LVA,0.29539 MAR,1.0 -MDA,0.857278 +MDA,0.853737 MDG,1.0 MEX,1.0 MKD,1.0 @@ -128,13 +128,13 @@ PAK,1.0 PAN,1.0 PER,1.0 PHL,1.0 -PNG,0.986016 +PNG,0.98085 POL,1.0 PRI,1.0 PRT,1.0 PRY,1.0 PSE,1.0 -ROU,0.753901 +ROU,0.754488 RUS,1.0 RWA,1.0 SAU,1.0 @@ -148,7 +148,7 @@ SRB,1.0 SSD,1.0 STP,1.0 SUR,1.0 -SVK,0.954113 +SVK,0.953837 SVN,1.0 SWE,1.0 SWZ,1.0 diff --git a/data/curated/calibration/gbd-anchored/food_demand.csv b/data/curated/calibration/gbd-anchored/food_demand.csv index bb51a192..8dab2a91 100644 --- a/data/curated/calibration/gbd-anchored/food_demand.csv +++ b/data/curated/calibration/gbd-anchored/food_demand.csv @@ -1,23 +1,23 @@ food,multiplier,net_slack_mt,consumption_mt -apple,0.9964443109117379,0.3678209779318422,103.445765026554 -banana,1.0,0.0,76.84232396420703 +apple,0.9974556108917725,0.26339260971872136,103.5189974941386 +banana,1.0,0.0,76.8967240014681 barley-hulled,1.0,0.0,2.200347095287043 -buckwheat,0.7434199045026576,0.9534211812883768,3.715881309656197 -cabbage,1.0575776224891007,-12.674407623708248,220.1273181452998 -carrot,1.026181295227714,-3.1852073669433594,121.65965584359947 -cassava,1.0,0.0,149.68125092926766 +buckwheat,0.7430989505841524,0.9546138079554112,3.715881309656197 +cabbage,1.0562413341082353,-12.349145397543907,219.57419028820004 +carrot,1.0269055104626206,-3.265090137720108,121.35395618143957 +cassava,1.0,0.0,149.35682562129477 chickpea,1.0,0.0,4.478103744486589 -citrus,1.0512842253806731,-6.784599781036377,132.29408713255543 -cocoa-powder,0.9075724784292891,0.4894329770177137,5.295316467436351 +citrus,1.0513920829503725,-6.80368185043335,132.38774262182415 +cocoa-powder,0.902422148467906,0.5167018563806778,5.2952780602136045 coconut,1.0,0.0,2.927419820128307 coconut-oil,1.0,0.0,0.9847869982731936 -coffee-green,0.8670346770617218,1.5567073001293465,11.707618691318203 +coffee-green,0.8670342455798214,1.5567072983831167,11.707580686255824 cottonseed-oil,1.0,0.0,3.8390739116130135 cowpea,1.0,0.0,2.4007039731534405 -dairy,0.9741199909093337,20.756315441802144,802.0211804828141 -dairy-buffalo,0.9952668258817422,0.6550484308972955,138.39516876645212 +dairy,0.9741199909087531,20.756315442267805,802.0211804828141 +dairy-buffalo,0.9952668259961427,0.6550484150648117,138.39516876645212 dry-pea,1.0,0.0,1.9984850413002277 -eggs,0.9999999933922069,5.425278004622669e-07,82.1042355971731 +eggs,1.0,0.0,82.1042355971731 flour-white,1.0,0.0,275.431504257489 flour-wholemeal,1.0,0.0,48.923026258606114 foxtail-millet,1.0,0.0,2.8788443399389507 @@ -26,21 +26,21 @@ groundnut,1.0,0.0,8.565938512935645 groundnut-oil,1.0,0.0,3.4693732594732865 maize,1.0,0.0,68.3637765974645 maize-whole,1.0,0.0,24.219736276596677 -mango,1.0328161050711082,-1.5924477654043585,48.526409881786094 -meat-cattle,0.952400557178112,2.308293342590332,48.49412526166998 -meat-chicken,0.9895527158773688,0.8225271608680487,78.7311947500566 -meat-pig,1.0184832063942477,-1.3977279365062714,75.62150779971125 -meat-sheep,1.0872487686362589,-0.8913660735124722,10.21637425312656 +mango,1.0315600282706816,-1.5325790643692017,48.56076335625221 +meat-cattle,0.9524005589809676,2.3082932551624253,48.49412526166998 +meat-chicken,0.9895527156644442,0.822527177631855,78.7311947500566 +meat-pig,1.0184832060001492,-1.397727906703949,75.62150779971125 +meat-sheep,1.0872487675309466,-0.891366062220186,10.21637425312656 oat,1.0,0.0,1.3277868090137872 -olive-oil,1.1709586557719431,-0.7189532550983131,4.205421783717043 -onion,0.9808766096716237,5.489760513883084,287.07046290516155 -palm-oil,0.5,39.40290946650384,23.789298962261455 +olive-oil,1.1718251246498517,-0.7225971221923828,4.205421783717043 +onion,0.9805990659980423,5.555440579424612,286.34913034929195 +palm-oil,0.5,39.35154621107904,23.789298962261455 pearl-millet,1.0,0.0,2.8788443399389507 phaseolus-bean,1.0,0.0,7.570616604105453 pigeon-pea,1.0,0.0,0.24040035817802163 -plantain,0.9212759041820071,1.9343916127127159,24.57178571075565 -potato,0.9688399882094009,8.087450784165412,259.54581912595313 -rapeseed-oil,0.9119021303172428,1.0467587411403656,11.881771317624043 +plantain,0.9232684359230778,1.8813530429906677,24.518632789820913 +potato,0.9681067061935169,8.260996837401763,259.0198706827359 +rapeseed-oil,0.9126010839113848,1.0384539343731376,11.881771317624043 rendered-fat,1.0,0.0,8.840820645729764 rice-brown,1.0,0.0,42.93316995531495 rice-white,1.0,0.0,352.6840182970045 @@ -49,12 +49,12 @@ sesame-oil,1.0,0.0,1.2538875761297277 sesame-seed,1.0,0.0,1.130570922832021 sorghum,1.0,0.0,9.408891627230332 soy,1.0,0.0,3.8865096387889935 -soybean-oil,0.9153797586953221,2.521884406339268,29.80237786440648 +soybean-oil,0.9218980539600212,2.327623707828934,29.80237786440648 sugar,1.0,0.0,154.03780619380996 -sunflower-oil,0.9807420733021773,0.25364919906132855,13.171158195861608 +sunflower-oil,0.9843609927598227,0.20598383838660084,13.171158195861608 sunflower-seed,1.0,0.0,1.9189327573915307 -sweet-potato,1.0,0.0,55.15774182416499 -tea-dried,0.9839852994777435,0.10687885975221434,6.673796965711517 -tomato,0.9262073745814126,10.369870988553885,140.52719942854674 -watermelon,1.2332586330181834,-12.692658424377441,54.414528029014036 -yam,0.9337839051883436,2.29566615668341,34.669307563563734 +sweet-potato,1.0,0.0,55.038199765389436 +tea-dried,0.9840023658971014,0.10676424254961603,6.673751997507679 +tomato,0.9282407769713135,10.058784053235286,140.17409370798487 +watermelon,1.2329239033644894,-12.683417320251465,54.45305156338509 +yam,0.921346543511107,2.726858277893598,34.669274557293875 diff --git a/data/curated/calibration/gbd-anchored/food_waste.yaml b/data/curated/calibration/gbd-anchored/food_waste.yaml index c73f5f71..99668cf0 100644 --- a/data/curated/calibration/gbd-anchored/food_waste.yaml +++ b/data/curated/calibration/gbd-anchored/food_waste.yaml @@ -10,26 +10,26 @@ # `tools/calibrate food_waste` instead. fruits: baseline_consumption_mt: 303.5781328359153 - baseline_negative_slack_mt: 111.9449801093142 + baseline_negative_slack_mt: 112.23914675923879 baseline_positive_slack_mt: 0.0 - waste_retention_multiplier: 0.7305926514752747 + waste_retention_multiplier: 0.7300757994749124 oil: baseline_consumption_mt: 101.94318941038546 - baseline_negative_slack_mt: 0.345420871861279 - baseline_positive_slack_mt: 51.40532734505882 - waste_retention_multiplier: 2.0034711505589695 + baseline_negative_slack_mt: 0.34906473755836487 + baseline_positive_slack_mt: 51.10373346695246 + waste_retention_multiplier: 1.9915244288011165 starchy_vegetable: baseline_consumption_mt: 548.2428959218498 baseline_negative_slack_mt: 0.0 - baseline_positive_slack_mt: 26.521615269138465 - waste_retention_multiplier: 1.0508348350980776 + baseline_positive_slack_mt: 27.652448822372435 + waste_retention_multiplier: 1.0531174726244803 stimulants: baseline_consumption_mt: 23.68795094791676 baseline_negative_slack_mt: 0.0 - baseline_positive_slack_mt: 2.1642379899412845 - waste_retention_multiplier: 1.1005513311837463 + baseline_positive_slack_mt: 2.191513587999907 + waste_retention_multiplier: 1.101947757728743 vegetables: baseline_consumption_mt: 514.297463798519 - baseline_negative_slack_mt: 255.08714949349815 + baseline_negative_slack_mt: 253.15391388688295 baseline_positive_slack_mt: 0.0 - waste_retention_multiplier: 0.6684530141536886 + waste_retention_multiplier: 0.6701368695820398 diff --git a/data/curated/calibration/gbd-anchored/grassland_cost.csv b/data/curated/calibration/gbd-anchored/grassland_cost.csv index 391e5b1c..39885ae2 100644 --- a/data/curated/calibration/gbd-anchored/grassland_cost.csv +++ b/data/curated/calibration/gbd-anchored/grassland_cost.csv @@ -1,175 +1,175 @@ country,correction_bnusd_per_mha -AFG,1.0285317042502129e-10 -AGO,-0.3685210794210434 -ALB,0.08087117224931717 -ARE,0.0 -ARG,-0.16487210243940353 -ARM,0.0 -ATG,0.0 -AUS,-0.00915704108774662 -AUT,1.834300888958751e-08 -AZE,1.0076294541358948 -BDI,-0.48811182379722595 -BEL,0.08367127552628517 -BEN,3.3643276691436768 -BFA,1.547168254852295 -BGD,0.0 -BGR,-0.3005576878786087 -BHS,-0.0316774295642972 -BIH,-0.4430038630962372 -BLR,-0.012004621792584658 -BLZ,-0.8535727262496948 -BOL,-0.5958932638168335 -BRA,-0.3751921057701111 -BRB,0.9579221606254578 -BRN,7.141561985015869 -BTN,-0.30104151368141174 -BWA,0.0 -CAF,-1.1816822631516288e-08 -CAN,-0.21679558604955673 -CHE,-0.07974380627274513 -CHL,-0.0009876639232970774 -CHN,-5.9879887714942015e-09 -CIV,-0.4987214505672455 -CMR,4.544210910797119 -COD,-0.7558623552322388 -COG,-0.8903338015079498 -COL,-0.7695494294166565 -COM,1.513160526752472 -CPV,0.0 -CRI,-0.040161214768886566 -CUB,-0.1725609302520752 -CYP,0.3170135170221329 -CZE,-0.19046077877283096 -DEU,-0.2572143077850342 -DJI,0.0 -DNK,1.8209227323532104 -DOM,-1.4828121619459012e-08 -DZA,-1.622208756213439e-10 -ECU,-0.10834560729563236 -EGY,0.0 -ERI,-0.036436695605516434 -ESP,-1.80058634668967e-10 -EST,-0.25669121742248535 -ETH,1.3066284942908624e-08 -FIN,1.7559711933135986 -FJI,-0.7943975925445557 -FRA,-1.0104092229568096e-08 -GAB,-0.814353346824646 -GBR,0.0 -GEO,-0.2082834094762802 -GHA,-0.5135024040937424 -GIN,-0.15879005938768387 -GMB,1.2610795497894287 -GNB,1.1563142538070679 -GNQ,-0.7098988890647888 -GRC,-0.09635294415056705 -GRD,0.692780077457428 -GTM,-4.2628794716392804e-08 -GUF,-1.0722496509552002 -GUY,-1.106971263885498 -HND,-0.5874203741550446 -HRV,-0.18356122076511383 -HTI,-1.6403337355086478e-08 -HUN,-0.4170793741941452 -IDN,-0.5079740881919861 -IND,1.5702753067016602 -IRL,-9.13200892682653e-09 -IRN,0.0 -IRQ,0.0 -ISL,-0.08946210891008377 -ISR,0.7338600158691406 -ITA,0.29519903659820557 -JAM,3.888116955757141 -JOR,0.0 -JPN,-0.4974181354045868 -KAZ,6.031010413298787e-11 -KEN,-0.17468049377202988 -KGZ,-2.072970683997255e-09 -KHM,-0.4191155880689621 -KOR,3.2005810737609863 -LAO,-3.513972668400811e-09 -LBN,0.5361965000629425 -LBR,-0.7553918659687042 -LBY,0.0 -LKA,-1.4365466594057352e-08 -LSO,-0.4911396652460098 -LTU,-0.016819098964333534 -LUX,0.061516094487160444 -LVA,-0.3037813901901245 -MAR,0.0 -MDA,-0.36603914201259613 -MDG,-0.6528348624706268 -MEX,1.0218221824231932e-08 -MKD,-0.27286694943904877 -MLI,-0.04440104402601719 -MLT,0.0 -MMR,4.39470548840859e-09 -MNE,-0.15099866688251495 -MNG,-0.05928472615778446 -MOZ,-0.5656761825084686 -MRT,-2.2212138378607804e-11 -MUS,-0.2207227498292923 -MWI,-0.19057554006576538 -MYS,-9.89881137059001e-08 -NAM,-0.006840692600235343 -NER,0.0 -NGA,-0.19652122259140015 -NIC,-0.5845303535461426 -NLD,0.7907610237598419 -NOR,0.598862886428833 -NPL,0.0 -NZL,0.0 -OMN,0.0 -PAK,1.424497075852571e-09 -PAN,-0.10266764834523201 -PER,-0.6414314210414886 -PHL,-0.12064796686172485 -PNG,-0.7848113179206848 -POL,0.02005419973284006 -PRI,2.838124757431615e-08 -PRT,-0.0998427290469408 -PRY,-0.5369450151920319 -PSE,0.7265221178531647 -ROU,-0.4245498776435852 -RUS,-6.801293306324396e-09 -RWA,6.642067909240723 -SAU,0.0 -SDN,-0.06754493713378906 -SEN,0.3892466574907303 -SLB,-0.18894267082214355 -SLE,-0.41614776849746704 -SLV,3.7280293703079224 -SOM,3.410516868918023e-09 -SRB,-0.295737162232399 -SSD,-0.3232929855585098 -STP,0.5782524645328522 -SUR,6.86613392829895 -SVK,-0.4092821776866913 -SVN,-0.20204976946115494 -SWE,1.8130713105201721 -SWZ,2.0045294313320028e-09 -SYR,0.14624850451946259 -TCD,-0.07812828943133354 -TGO,-0.46927402913570404 -THA,-3.208625720674263e-08 -TJK,0.40645886957645416 -TKM,-9.089148877983177e-10 -TLS,-0.3597917854785919 -TTO,-1.403013705214562e-09 -TUN,0.37630587816238403 -TUR,1.1556978225708008 -TWN,-0.30179035663604736 -TZA,-0.06839879974722862 -UGA,-0.6885234415531158 -UKR,0.058694761246442795 -URY,-0.21174990385770798 -USA,-0.40415892004966736 -UZB,0.9957038164138794 -VEN,-0.26628775894641876 -VNM,5.844109058380127 -VUT,-0.23864496499300003 -YEM,0.0 -ZAF,0.0 -ZMB,-0.15246352553367615 -ZWE,1.0790897011756897 +AFG,-4.0427408296017653e-10 +AGO,-0.38177555799484253 +ALB,-0.20878468453884125 +ARE,-0.0 +ARG,-0.1856212317943573 +ARM,-0.0 +ATG,-0.0 +AUS,-0.04442281275987625 +AUT,9.24111176470177e-10 +AZE,0.7331931293010712 +BDI,-0.49132993817329407 +BEL,0.08877222612500191 +BEN,2.2527434825897217 +BFA,1.0416541993618011 +BGD,-0.0 +BGR,-0.30829504132270813 +BHS,-0.03126266412436962 +BIH,-0.4584890455007553 +BLR,-0.01833034586161375 +BLZ,-0.7399452030658722 +BOL,-0.6221835613250732 +BRA,-0.5207413733005524 +BRB,0.6817097067832947 +BRN,-3.437714735099462e-08 +BTN,-0.3201640695333481 +BWA,-0.0 +CAF,-1.7237690386195936e-08 +CAN,-0.21300436556339264 +CHE,-0.12198027223348618 +CHL,-0.03965021949261427 +CHN,-4.022972177608608e-09 +CIV,-0.5443657338619232 +CMR,2.140450656414032 +COD,-0.7532423138618469 +COG,-0.8099571466445923 +COL,-0.6204277873039246 +COM,1.1216293573379517 +CPV,-0.0 +CRI,-0.04483989253640175 +CUB,-0.17085150629281998 +CYP,0.2356496825814247 +CZE,-0.2163601964712143 +DEU,-0.2619142234325409 +DJI,-0.0 +DNK,1.4884825348854065 +DOM,-0.4018055945634842 +DZA,-0.0011151906801387668 +ECU,-0.19687405228614807 +EGY,-0.0 +ERI,-0.04815981723368168 +ESP,-0.03535165823996067 +EST,-0.26680906116962433 +ETH,-2.0605006589846653e-09 +FIN,1.267688274383545 +FJI,-0.8799494802951813 +FRA,-2.9409451540729492e-09 +GAB,-0.8222148716449738 +GBR,-0.028139030560851097 +GEO,-0.20096751302480698 +GHA,-0.5431598126888275 +GIN,-0.37204913794994354 +GMB,0.9210774004459381 +GNB,0.8557053804397583 +GNQ,-0.7101413011550903 +GRC,-0.1523909792304039 +GRD,0.47368717193603516 +GTM,-2.610952076764761e-08 +GUF,-0.9834447503089905 +GUY,-1.1032667756080627 +HND,-0.6275273263454437 +HRV,-0.17099863290786743 +HTI,1.2459450360324809e-08 +HUN,-0.4085644632577896 +IDN,-0.562181144952774 +IND,1.2133966088294983 +IRL,-1.1858245052565053e-08 +IRN,-0.0 +IRQ,-0.0 +ISL,-0.09247573837637901 +ISR,0.5439601242542267 +ITA,0.4403725564479828 +JAM,2.761610507965088 +JOR,-0.0 +JPN,-0.4728243798017502 +KAZ,-4.997817021346052e-10 +KEN,-0.2179667055606842 +KGZ,-1.2954760597594372e-09 +KHM,-2.9558109737592986e-08 +KOR,2.4750083684921265 +LAO,4.666451669521621e-10 +LBN,0.39881160855293274 +LBR,-0.7191254794597626 +LBY,2.6584022136399987e-10 +LKA,-0.1928924173116684 +LSO,-0.43739476799964905 +LTU,-0.07347145304083824 +LUX,0.008767670951783657 +LVA,-0.2936539202928543 +MAR,-0.0 +MDA,-0.3453979641199112 +MDG,-0.6868391931056976 +MEX,-0.0679156482219696 +MKD,-0.27600471675395966 +MLI,-0.037232923321425915 +MLT,-0.0 +MMR,2.074207365512848 +MNE,-0.3422906696796417 +MNG,-0.038229407742619514 +MOZ,-0.5160333812236786 +MRT,-0.033999696373939514 +MUS,-3.315589225394433e-08 +MWI,-3.3964567824895653e-09 +MYS,1.4313578766689261e-08 +NAM,-0.0047507211565971375 +NER,-0.0 +NGA,-0.14038769900798798 +NIC,-0.5779949724674225 +NLD,0.8321638405323029 +NOR,1.0499045252799988 +NPL,0.021747911348938942 +NZL,-0.0 +OMN,-0.0 +PAK,-0.0 +PAN,-0.6221974492073059 +PER,-0.6955994665622711 +PHL,-0.11315194144845009 +PNG,-0.8056034743785858 +POL,-0.036101678386330605 +PRI,-2.7517170764213006e-09 +PRT,-0.18238242715597153 +PRY,-0.6874429285526276 +PSE,0.5131678581237793 +ROU,-0.4107300937175751 +RUS,-0.0 +RWA,1.4384679225543096e-08 +SAU,-0.0 +SDN,-0.07489163428544998 +SEN,0.28525300323963165 +SLB,-2.5197355313366643e-10 +SLE,-0.3628302663564682 +SLV,2.6754162311553955 +SOM,-0.05690718814730644 +SRB,-0.2917206585407257 +SSD,-0.2865627706050873 +STP,0.3985329568386078 +SUR,-0.13022971898317337 +SVK,-0.4101209193468094 +SVN,-0.27246034145355225 +SWE,1.1129473447799683 +SWZ,-2.0683636137164285e-08 +SYR,0.10907374322414398 +TCD,-0.08657535165548325 +TGO,-0.30542606115341187 +THA,-1.830725793183774e-08 +TJK,0.3043214976787567 +TKM,-0.0 +TLS,-0.22256753593683243 +TTO,-3.4331349985095017e-09 +TUN,0.6830682754516602 +TUR,0.7784259021282196 +TWN,-0.4033449739217758 +TZA,-0.40795499086380005 +UGA,-2.739427173992226e-08 +UKR,-0.007475577760487795 +URY,-0.38374826312065125 +USA,-0.21028513461351395 +UZB,-0.0 +VEN,-0.340562105178833 +VNM,4.418777227401733 +VUT,-0.19533131271600723 +YEM,-6.187674372881702e-09 +ZAF,-0.04584670439362526 +ZMB,-0.39802390336990356 +ZWE,0.3660371880978346 diff --git a/data/curated/calibration/gbd-anchored/grassland_yield.csv b/data/curated/calibration/gbd-anchored/grassland_yield.csv index ab4cfcdd..6adf723d 100644 --- a/data/curated/calibration/gbd-anchored/grassland_yield.csv +++ b/data/curated/calibration/gbd-anchored/grassland_yield.csv @@ -2,177 +2,177 @@ # # SPDX-License-Identifier: CC-BY-4.0 country,yield_correction -AFG,0.395294 -AGO,0.071585 -ALB,0.144938 +AFG,0.3953 +AGO,0.071477 +ALB,0.145104 ARE,1.0 -ARG,0.345713 -ARM,0.550552 +ARG,0.200708 +ARM,0.550888 ATG,1.0 -AUS,0.236373 -AUT,0.600427 +AUS,0.249267 +AUT,0.589604 AZE,1.0 -BDI,0.442052 -BEL,0.928966 +BDI,0.437994 +BEL,0.932016 BEN,1.0 BFA,1.0 BGD,1.0 -BGR,0.074007 -BHS,0.669108 -BIH,0.01467 -BLR,0.422193 -BLZ,0.102024 -BOL,0.111315 -BRA,0.229034 +BGR,0.072907 +BHS,0.669107 +BIH,0.014664 +BLR,0.420747 +BLZ,0.101966 +BOL,0.11001 +BRA,0.23009 BRB,1.0 BRN,1.0 -BTN,0.03586 +BTN,0.035842 BWA,1.0 -CAF,0.583977 -CAN,0.051832 -CHE,0.40712 -CHL,0.686938 -CHN,0.290638 -CIV,0.033413 +CAF,0.598793 +CAN,0.048283 +CHE,0.409804 +CHL,0.711568 +CHN,0.287527 +CIV,0.032799 CMR,1.0 -COD,0.022128 -COG,0.00797 -COL,0.103539 +COD,0.022132 +COG,0.00757 +COL,0.104125 COM,1.0 CPV,1.0 -CRI,0.380109 -CUB,0.277685 +CRI,0.376606 +CUB,0.276615 CYP,1.0 -CZE,0.232729 -DEU,0.150859 +CZE,0.232716 +DEU,0.156978 DJI,1.0 DNK,1.0 -DOM,0.567981 -DZA,0.791718 -ECU,0.36376 +DOM,0.550096 +DZA,0.71264 +ECU,0.354057 EGY,1.0 -ERI,0.518486 -ESP,0.429751 +ERI,0.518822 +ESP,0.428108 EST,0.0 -ETH,0.551195 +ETH,0.551758 FIN,1.0 -FJI,0.041419 -FRA,0.488102 -GAB,0.007952 -GBR,0.40073 -GEO,0.122798 -GHA,0.178956 -GIN,0.173001 +FJI,0.040158 +FRA,0.490452 +GAB,0.005941 +GBR,0.400867 +GEO,0.121039 +GHA,0.16792 +GIN,0.173016 GMB,1.0 GNB,1.0 -GNQ,0.077495 -GRC,0.075784 +GNQ,0.077511 +GRC,0.073372 GRD,1.0 -GTM,0.571477 +GTM,0.584133 GUF,0.0 -GUY,0.018762 -HND,0.186551 -HRV,0.284196 -HTI,0.92669 +GUY,0.01813 +HND,0.148916 +HRV,0.283024 +HTI,0.925315 HUN,0.0 -IDN,0.144268 +IDN,0.144689 IND,1.0 -IRL,0.633733 -IRN,0.769453 +IRL,0.635826 +IRN,0.806529 IRQ,1.0 -ISL,0.062237 +ISL,0.063932 ISR,1.0 ITA,1.0 JAM,1.0 JOR,1.0 JPN,0.0 -KAZ,0.256835 -KEN,0.467676 -KGZ,0.842055 -KHM,0.398086 +KAZ,0.257373 +KEN,0.467622 +KGZ,0.841994 +KHM,0.398064 KOR,1.0 -LAO,0.446981 +LAO,0.400714 LBN,1.0 -LBR,0.016113 -LBY,0.894499 -LKA,0.193473 -LSO,0.043373 -LTU,0.341665 -LUX,0.783687 +LBR,0.01603 +LBY,0.883798 +LKA,0.192633 +LSO,0.048298 +LTU,0.340422 +LUX,0.787131 LVA,0.0 MAR,1.0 MDA,0.0 -MDG,0.016111 -MEX,0.371319 -MKD,0.104967 -MLI,0.387873 +MDG,0.016071 +MEX,0.379061 +MKD,0.104998 +MLI,0.392024 MLT,1.0 MMR,1.0 -MNE,0.077905 -MNG,0.245052 -MOZ,0.00289 -MRT,0.66052 +MNE,0.077909 +MNG,0.245828 +MOZ,0.002894 +MRT,0.662616 MUS,0.811415 -MWI,0.76798 -MYS,0.442334 -NAM,0.29193 +MWI,0.759081 +MYS,0.436504 +NAM,0.327938 NER,1.0 -NGA,0.665316 -NIC,0.295306 +NGA,0.680682 +NIC,0.299317 NLD,1.0 NOR,1.0 NPL,1.0 -NZL,0.560406 +NZL,0.558077 OMN,1.0 PAK,1.0 -PAN,0.312528 -PER,0.084126 -PHL,0.384506 +PAN,0.254535 +PER,0.09729 +PHL,0.380175 PNG,0.0 -POL,0.560559 -PRI,0.459587 -PRT,0.081039 -PRY,0.068489 +POL,0.560699 +PRI,0.43587 +PRT,0.112791 +PRY,0.069461 PSE,1.0 ROU,0.0 -RUS,0.15561 -RWA,1.0 +RUS,0.154765 +RWA,0.794253 SAU,1.0 -SDN,0.419271 +SDN,0.418012 SEN,1.0 -SLB,0.408618 -SLE,0.093191 +SLB,0.575706 +SLE,0.091711 SLV,1.0 -SOM,0.555429 -SRB,0.136713 -SSD,0.142093 +SOM,0.542251 +SRB,0.141601 +SSD,0.141785 STP,1.0 -SUR,1.0 +SUR,0.814601 SVK,0.0 -SVN,0.252012 +SVN,0.252093 SWE,1.0 -SWZ,0.283263 +SWZ,0.283244 SYR,1.0 -TCD,0.695865 -TGO,0.206827 -THA,0.898892 +TCD,0.697183 +TGO,0.199885 +THA,0.860523 TJK,1.0 TKM,1.0 -TLS,0.234166 -TTO,0.839948 +TLS,0.233632 +TTO,0.743021 TUN,1.0 TUR,1.0 -TWN,0.107613 -TZA,0.407102 -UGA,0.425128 -UKR,0.166781 -URY,0.242289 -USA,0.031197 +TWN,0.108669 +TZA,0.405365 +UGA,0.394319 +UKR,0.167267 +URY,0.270061 +USA,0.03083 UZB,1.0 -VEN,0.190094 +VEN,0.188842 VNM,1.0 -VUT,0.176534 -YEM,0.592561 -ZAF,0.346808 -ZMB,0.203235 +VUT,0.163781 +YEM,0.586928 +ZAF,0.340447 +ZMB,0.202565 ZWE,1.0 diff --git a/data/curated/calibration/gbd-anchored/multi_crop_cost.csv b/data/curated/calibration/gbd-anchored/multi_crop_cost.csv index 0ecd181b..9ec86a0f 100644 --- a/data/curated/calibration/gbd-anchored/multi_crop_cost.csv +++ b/data/curated/calibration/gbd-anchored/multi_crop_cost.csv @@ -1,101 +1,102 @@ combination,country,correction_bnusd_per_mha -cotton_wheat,AFG,0.15957194566726685 -cotton_wheat,AGO,1.2010616064071655 +cotton_wheat,AFG,-1.6016085147857666 +cotton_wheat,AGO,-1.9636707305908203 cotton_wheat,ALB,7.5 cotton_wheat,ARG,7.5 cotton_wheat,ARM,7.5 -cotton_wheat,AUS,-7.5 +cotton_wheat,AUS,-6.347338676452637 cotton_wheat,AZE,7.5 -cotton_wheat,BDI,-0.508621871471405 -cotton_wheat,BGD,4.844290018081665 +cotton_wheat,BDI,-1.0001932382583618 +cotton_wheat,BGD,-1.6351697444915771 cotton_wheat,BGR,7.5 cotton_wheat,BOL,7.5 -cotton_wheat,BRA,6.54271388053894 -cotton_wheat,BWA,4.453519642353058 -cotton_wheat,CHN,1.2160378098487854 -cotton_wheat,COD,-1.0064157247543335 +cotton_wheat,BRA,-2.795017957687378 +cotton_wheat,BWA,-1.389197826385498 +cotton_wheat,CHN,-6.734447956085205 +cotton_wheat,COD,-1.943974256515503 cotton_wheat,DZA,7.5 -cotton_wheat,EGY,0.7566313743591309 -cotton_wheat,ESP,2.4812554121017456 -cotton_wheat,ETH,-1.1835752129554749 +cotton_wheat,EGY,-3.046511173248291 +cotton_wheat,ESP,-1.8644442558288574 +cotton_wheat,ETH,-2.5176395177841187 cotton_wheat,GEO,7.5 -cotton_wheat,GRC,1.3865758180618286 -cotton_wheat,HND,-0.029271302744746208 -cotton_wheat,IND,-0.7118860483169556 -cotton_wheat,IRN,0.5818364024162292 -cotton_wheat,IRQ,7.5 -cotton_wheat,ISR,2.467694878578186 -cotton_wheat,JOR,2.618316411972046 -cotton_wheat,KAZ,1.5115489959716797 -cotton_wheat,KEN,1.0903679132461548 -cotton_wheat,KGZ,2.321509003639221 +cotton_wheat,GRC,-2.5224956274032593 +cotton_wheat,HND,-2.0235610008239746 +cotton_wheat,IND,-1.663497805595398 +cotton_wheat,IRN,-2.413421869277954 +cotton_wheat,IRQ,-1.8790355920791626 +cotton_wheat,ISR,-0.5844569504261017 +cotton_wheat,JOR,-2.261714220046997 +cotton_wheat,KAZ,-2.105399250984192 +cotton_wheat,KEN,-0.7825355529785156 +cotton_wheat,KGZ,-1.4670000076293945 cotton_wheat,KOR,7.5 cotton_wheat,LBN,7.5 -cotton_wheat,LSO,4.415732979774475 -cotton_wheat,MAR,1.087628185749054 -cotton_wheat,MDG,-0.12449101358652115 -cotton_wheat,MEX,5.469664812088013 -cotton_wheat,MKD,3.2964072227478027 +cotton_wheat,LSO,3.2752625644207 +cotton_wheat,MAR,-3.012405753135681 +cotton_wheat,MDG,-1.7472525835037231 +cotton_wheat,MEX,-3.5203187465667725 +cotton_wheat,MKD,-1.780644416809082 cotton_wheat,MMR,7.5 -cotton_wheat,MOZ,0.4938295781612396 -cotton_wheat,MWI,7.5 -cotton_wheat,NAM,4.194038838148117 -cotton_wheat,NER,7.5 -cotton_wheat,NGA,-7.5 +cotton_wheat,MOZ,-1.4387832283973694 +cotton_wheat,MWI,2.7791829705238342 +cotton_wheat,NAM,-2.076709508895874 +cotton_wheat,NER,-0.8645138144493103 +cotton_wheat,NGA,-1.9162575006484985 cotton_wheat,NPL,7.5 -cotton_wheat,PAK,1.2053667306900024 +cotton_wheat,OMN,-0.0 +cotton_wheat,PAK,-1.3370583057403564 cotton_wheat,PRT,7.5 -cotton_wheat,PRY,2.0514886379241943 -cotton_wheat,PSE,1.3464239835739136 -cotton_wheat,ROU,0.0 +cotton_wheat,PRY,-0.814936101436615 +cotton_wheat,PSE,-1.8815619945526123 +cotton_wheat,ROU,-0.0 cotton_wheat,RUS,7.5 -cotton_wheat,RWA,-0.2725582420825958 -cotton_wheat,SDN,-7.5 -cotton_wheat,SOM,-7.5 -cotton_wheat,SWZ,-0.8359448909759521 -cotton_wheat,SYR,1.9367197751998901 -cotton_wheat,TJK,0.17783039808273315 -cotton_wheat,TKM,0.5806401371955872 +cotton_wheat,RWA,-1.6137886047363281 +cotton_wheat,SDN,-1.7738453149795532 +cotton_wheat,SOM,2.7912174463272095 +cotton_wheat,SWZ,-1.798463225364685 +cotton_wheat,SYR,-1.6039836406707764 +cotton_wheat,TJK,-3.2277636528015137 +cotton_wheat,TKM,-1.6811714172363281 cotton_wheat,TUN,7.5 -cotton_wheat,TUR,4.218400955200195 -cotton_wheat,TZA,-0.6822943687438965 -cotton_wheat,UGA,-0.19741022400557995 +cotton_wheat,TUR,-1.2099840641021729 +cotton_wheat,TZA,-1.5156616568565369 +cotton_wheat,UGA,-1.794050395488739 cotton_wheat,URY,7.5 -cotton_wheat,USA,2.4673547744750977 -cotton_wheat,UZB,0.4927860200405121 -cotton_wheat,YEM,-7.5 -cotton_wheat,ZAF,2.787626266479492 +cotton_wheat,USA,-1.561903715133667 +cotton_wheat,UZB,-1.8172263503074646 +cotton_wheat,YEM,-2.3821864128112793 +cotton_wheat,ZAF,-3.1744508743286133 cotton_wheat,ZMB,7.5 -cotton_wheat,ZWE,6.100391387939453 +cotton_wheat,ZWE,3.732309103012085 double_rice,AFG,7.5 double_rice,AGO,7.5 -double_rice,ALB,0.0 +double_rice,ALB,-0.0 double_rice,ARG,7.5 double_rice,AUS,7.5 double_rice,AZE,7.5 -double_rice,BDI,-6.592018127441406 -double_rice,BEN,-2.16798198223114 -double_rice,BFA,-7.317521095275879 -double_rice,BGD,0.11906862538307905 -double_rice,BLZ,2.3788822889328003 +double_rice,BDI,3.4612558484077454 +double_rice,BEN,-2.1600229740142822 +double_rice,BFA,-2.2092283964157104 +double_rice,BGD,-0.8059143722057343 +double_rice,BLZ,2.121865391731262 double_rice,BOL,7.5 double_rice,BRA,7.5 -double_rice,BRN,-2.855687379837036 +double_rice,BRN,-2.9540343284606934 double_rice,BTN,7.5 -double_rice,BWA,0.0 +double_rice,BWA,-0.0 double_rice,CAF,7.5 double_rice,CHL,7.5 -double_rice,CHN,-2.981346845626831 +double_rice,CHN,-3.8152025938034058 double_rice,CIV,7.5 -double_rice,CMR,7.5 +double_rice,CMR,-2.14290189743042 double_rice,COD,7.5 double_rice,COG,7.5 -double_rice,COL,-2.277894973754883 -double_rice,CRI,-1.5134159326553345 +double_rice,COL,-2.7943174839019775 +double_rice,CRI,-1.9651430249214172 double_rice,CUB,7.5 -double_rice,DOM,-2.330437660217285 +double_rice,DOM,2.0134849548339844 double_rice,DZA,7.5 -double_rice,ECU,-1.0004610419273376 +double_rice,ECU,-1.429942786693573 double_rice,EGY,7.5 double_rice,ERI,7.5 double_rice,ESP,7.5 @@ -104,390 +105,390 @@ double_rice,FJI,7.5 double_rice,FRA,7.5 double_rice,GAB,7.5 double_rice,GEO,7.5 -double_rice,GHA,-0.23847725987434387 -double_rice,GIN,-7.5 -double_rice,GMB,-7.5 -double_rice,GNB,-7.5 +double_rice,GHA,-0.8484333753585815 +double_rice,GIN,-1.1149972677230835 +double_rice,GMB,-0.03029663860797882 +double_rice,GNB,-2.5158348083496094 double_rice,GNQ,7.5 double_rice,GRC,7.5 -double_rice,GTM,5.48490297794342 -double_rice,GUF,0.0 +double_rice,GTM,2.2835614681243896 +double_rice,GUF,7.5 double_rice,GUY,7.5 double_rice,HND,7.5 -double_rice,HTI,2.5956857204437256 -double_rice,IDN,-2.4885209798812866 -double_rice,IND,2.3034749031066895 +double_rice,HTI,2.4255069494247437 +double_rice,IDN,-2.813283920288086 +double_rice,IND,-1.7255062460899353 double_rice,IRN,7.5 double_rice,IRQ,7.5 double_rice,ITA,7.5 -double_rice,JPN,-7.4698264598846436 +double_rice,JPN,-7.4767537117004395 double_rice,KEN,7.5 -double_rice,KHM,-4.303137898445129 -double_rice,KOR,7.5 -double_rice,LAO,-2.1252903938293457 +double_rice,KHM,-2.6360760927200317 +double_rice,KOR,0.0 +double_rice,LAO,-2.5891036987304688 double_rice,LBR,7.5 -double_rice,LKA,-0.31366080045700073 -double_rice,LSO,0.0 +double_rice,LKA,3.291220009326935 +double_rice,LSO,-0.0 double_rice,MAR,7.5 -double_rice,MDG,3.7404229445382953 +double_rice,MDG,3.556140199303627 double_rice,MEX,7.5 double_rice,MKD,7.5 -double_rice,MLI,-7.5 -double_rice,MMR,-2.452920436859131 +double_rice,MLI,-0.19956199079751968 +double_rice,MMR,-2.759416341781616 double_rice,MOZ,7.5 -double_rice,MRT,-7.5 +double_rice,MRT,-1.7148674726486206 double_rice,MUS,7.5 -double_rice,MWI,-7.5 -double_rice,MYS,-0.9995557069778442 +double_rice,MWI,-1.9178258180618286 +double_rice,MYS,-0.9908324480056763 double_rice,NAM,7.5 double_rice,NER,7.5 -double_rice,NGA,-3.361262798309326 -double_rice,NIC,5.933155536651611 -double_rice,NPL,5.158517837524414 +double_rice,NGA,-0.4380754977464676 +double_rice,NIC,-0.3926215171813965 +double_rice,NPL,3.6133005023002625 double_rice,PAK,7.5 -double_rice,PAN,-1.6340627670288086 +double_rice,PAN,-2.2472994327545166 double_rice,PER,7.5 -double_rice,PHL,-1.2978726625442505 +double_rice,PHL,-1.7187378406524658 double_rice,PNG,7.5 double_rice,PRI,7.5 double_rice,PRT,7.5 double_rice,PRY,7.5 -double_rice,RWA,-2.7989927530288696 +double_rice,RWA,-3.4841092824935913 double_rice,SAU,7.5 double_rice,SDN,7.5 -double_rice,SEN,-2.7050421237945557 +double_rice,SEN,0.23442745953798294 double_rice,SLB,7.5 -double_rice,SLE,-7.243507623672485 +double_rice,SLE,3.599737837910652 double_rice,SLV,7.5 double_rice,SOM,7.5 double_rice,SSD,7.5 double_rice,SUR,7.5 double_rice,SWZ,7.5 double_rice,SYR,7.5 -double_rice,TCD,-0.8257056474685669 -double_rice,TGO,0.14258075272664428 -double_rice,THA,-0.7501848936080933 +double_rice,TCD,-1.106724500656128 +double_rice,TGO,-0.24520094692707062 +double_rice,THA,-0.89333575963974 double_rice,TJK,7.5 double_rice,TKM,7.5 double_rice,TLS,7.5 double_rice,TTO,7.5 -double_rice,TUN,0.0 +double_rice,TUN,-0.0 double_rice,TUR,7.5 -double_rice,TWN,-0.9604698419570923 -double_rice,TZA,4.199115186929703 +double_rice,TWN,-1.9572516083717346 +double_rice,TZA,7.5 double_rice,UGA,7.5 double_rice,URY,7.5 double_rice,USA,7.5 double_rice,UZB,7.5 double_rice,VEN,7.5 -double_rice,VNM,-1.621755599975586 -double_rice,YEM,0.0 +double_rice,VNM,-2.058097720146179 +double_rice,YEM,-0.0 double_rice,ZAF,7.5 double_rice,ZMB,7.5 double_rice,ZWE,7.5 -maize_soybean,AFG,0.13687430508434772 -maize_soybean,AGO,-0.2613598108291626 +maize_soybean,AFG,-0.022748412564396858 +maize_soybean,AGO,-1.453369140625 maize_soybean,ALB,7.5 -maize_soybean,ARG,7.5 +maize_soybean,ARG,0.13026892486959696 maize_soybean,ARM,7.5 -maize_soybean,AUS,2.285356044769287 -maize_soybean,AUT,7.5 -maize_soybean,AZE,4.640923798084259 -maize_soybean,BDI,-0.971374124288559 +maize_soybean,AUS,1.7598615884780884 +maize_soybean,AUT,4.7173022627830505 +maize_soybean,AZE,0.5006841421127319 +maize_soybean,BDI,-1.0802985429763794 maize_soybean,BEL,7.5 -maize_soybean,BEN,-0.7208980917930603 -maize_soybean,BFA,-0.31730836629867554 +maize_soybean,BEN,-0.7781942188739777 +maize_soybean,BFA,-0.5925497114658356 maize_soybean,BGD,7.5 maize_soybean,BGR,7.5 maize_soybean,BIH,7.5 -maize_soybean,BLZ,-1.2057549953460693 -maize_soybean,BOL,0.0647062361240387 -maize_soybean,BRA,0.3150215446949005 -maize_soybean,BTN,1.0969263315200806 -maize_soybean,BWA,4.064399540424347 -maize_soybean,CAF,-1.5539294481277466 -maize_soybean,CAN,7.5 +maize_soybean,BLZ,-1.4516592025756836 +maize_soybean,BOL,3.6931674145162106 +maize_soybean,BRA,-0.05819132551550865 +maize_soybean,BTN,4.0696980357170105 +maize_soybean,BWA,0.20446154475212097 +maize_soybean,CAF,-7.5 +maize_soybean,CAN,-0.4329428970813751 maize_soybean,CHE,7.5 -maize_soybean,CHN,-1.4334770441055298 -maize_soybean,CIV,-0.8392142653465271 -maize_soybean,CMR,-0.9884616434574127 -maize_soybean,COD,-1.5485203266143799 -maize_soybean,COG,-1.4889176487922668 -maize_soybean,COL,-0.07628445327281952 -maize_soybean,CRI,-0.7993972301483154 +maize_soybean,CHN,-1.761126160621643 +maize_soybean,CIV,-0.941570907831192 +maize_soybean,CMR,-1.040739357471466 +maize_soybean,COD,-1.622969388961792 +maize_soybean,COG,-1.5172104239463806 +maize_soybean,COL,-0.3639393150806427 +maize_soybean,CRI,-0.8879108428955078 maize_soybean,CUB,7.5 maize_soybean,CZE,7.5 maize_soybean,DEU,7.5 -maize_soybean,DZA,0.0 -maize_soybean,ECU,-0.4734065532684326 -maize_soybean,EGY,-1.6842405796051025 -maize_soybean,ESP,7.5 -maize_soybean,ETH,0.2626522034406662 +maize_soybean,DZA,-0.0 +maize_soybean,ECU,-0.8186103105545044 +maize_soybean,EGY,-2.090902805328369 +maize_soybean,ESP,-0.9297317862510681 +maize_soybean,ETH,-0.03956172242760658 maize_soybean,FRA,7.5 -maize_soybean,GAB,-1.1092767715454102 -maize_soybean,GEO,0.5402394533157349 -maize_soybean,GHA,-0.6359098851680756 +maize_soybean,GAB,-1.2612962126731873 +maize_soybean,GEO,0.280986487865448 +maize_soybean,GHA,-0.7917667627334595 maize_soybean,GIN,7.5 -maize_soybean,GRC,3.498760938644409 -maize_soybean,GTM,3.0123302936553955 -maize_soybean,HND,-0.02142040804028511 +maize_soybean,GRC,2.78367817401886 +maize_soybean,GTM,-0.45622143149375916 +maize_soybean,HND,-0.27370449900627136 maize_soybean,HRV,7.5 -maize_soybean,HUN,7.5 -maize_soybean,IDN,0.7064895629882812 -maize_soybean,IND,-0.5969436764717102 -maize_soybean,IRN,-0.22185522317886353 -maize_soybean,IRQ,-0.23048602044582367 +maize_soybean,HUN,0.5368187390267849 +maize_soybean,IDN,0.14225086569786072 +maize_soybean,IND,-0.6644619703292847 +maize_soybean,IRN,-0.15079118311405182 +maize_soybean,IRQ,-0.6124063618481159 maize_soybean,ISR,7.5 maize_soybean,ITA,7.5 -maize_soybean,JOR,0.0 +maize_soybean,JOR,-0.0 maize_soybean,KAZ,7.5 -maize_soybean,KEN,-0.3480319082736969 -maize_soybean,KGZ,3.8359000459313393 -maize_soybean,KHM,-0.37067945301532745 -maize_soybean,KOR,-0.044323911890387535 -maize_soybean,LAO,0.32661522924900055 +maize_soybean,KEN,-0.438772976398468 +maize_soybean,KGZ,-0.3382623493671417 +maize_soybean,KHM,-0.6917684078216553 +maize_soybean,KOR,-0.4339219331741333 +maize_soybean,LAO,-0.08379296539351344 maize_soybean,LBN,7.5 -maize_soybean,LKA,-0.6501298546791077 +maize_soybean,LKA,-0.8904912769794464 maize_soybean,LSO,7.5 maize_soybean,LUX,7.5 maize_soybean,MAR,7.5 maize_soybean,MDA,7.5 -maize_soybean,MEX,2.211527109146118 +maize_soybean,MEX,0.1641877219080925 maize_soybean,MKD,7.5 -maize_soybean,MLI,-0.5398649573326111 -maize_soybean,MMR,-0.7718805074691772 +maize_soybean,MLI,-0.8014289140701294 +maize_soybean,MMR,-1.0643283128738403 maize_soybean,MNE,7.5 -maize_soybean,MOZ,7.5 -maize_soybean,MWI,-0.16863681375980377 -maize_soybean,NGA,-0.6313824951648712 -maize_soybean,NIC,1.6180334091186523 +maize_soybean,MOZ,3.9535765796899796 +maize_soybean,MWI,3.600542798638344 +maize_soybean,NGA,-0.7509967088699341 +maize_soybean,NIC,-0.4151580035686493 maize_soybean,NLD,7.5 -maize_soybean,NPL,7.5 -maize_soybean,PAK,-0.4839424192905426 +maize_soybean,NPL,3.7117144651710987 +maize_soybean,PAK,-0.7835625112056732 maize_soybean,PER,7.5 maize_soybean,POL,7.5 maize_soybean,PRT,7.5 -maize_soybean,PRY,0.0 -maize_soybean,ROU,4.643324792385101 +maize_soybean,PRY,-0.41487105190753937 +maize_soybean,ROU,4.206701397895813 maize_soybean,RUS,7.5 -maize_soybean,RWA,-0.2505238503217697 -maize_soybean,SDN,0.17269662022590637 -maize_soybean,SLV,-0.637964516878128 +maize_soybean,RWA,-0.35534021258354187 +maize_soybean,SDN,-0.33095285296440125 +maize_soybean,SLV,-0.8271979987621307 maize_soybean,SRB,7.5 maize_soybean,SSD,7.5 maize_soybean,SVK,7.5 maize_soybean,SVN,7.5 -maize_soybean,SWZ,-0.5656325221061707 +maize_soybean,SWZ,-0.7634835839271545 maize_soybean,SYR,7.5 -maize_soybean,TCD,-0.10343484580516815 -maize_soybean,TGO,-0.2898097485303879 -maize_soybean,THA,-0.1728121116757393 +maize_soybean,TCD,3.591791406273842 +maize_soybean,TGO,-0.3725236654281616 +maize_soybean,THA,-0.4974634647369385 maize_soybean,TJK,7.5 -maize_soybean,TKM,4.70890748500824 -maize_soybean,TLS,7.5 +maize_soybean,TKM,7.5 +maize_soybean,TLS,4.1178505420684814 maize_soybean,TUR,7.5 -maize_soybean,TWN,0.0 -maize_soybean,TZA,-0.18530809879302979 -maize_soybean,UGA,-1.319997251033783 +maize_soybean,TWN,-0.0 +maize_soybean,TZA,-0.34078487753868103 +maize_soybean,UGA,-1.4062016010284424 maize_soybean,UKR,7.5 -maize_soybean,URY,-0.08187820017337799 -maize_soybean,USA,-0.049517277628183365 +maize_soybean,URY,0.045277222990989685 +maize_soybean,USA,-0.6260630488395691 maize_soybean,UZB,7.5 -maize_soybean,VEN,-0.6624778509140015 -maize_soybean,VNM,0.09539355337619781 -maize_soybean,ZAF,2.829430341720581 -maize_soybean,ZMB,2.2089239358901978 -maize_soybean,ZWE,1.625107765197754 -rice_maize,AFG,-1.3318929076194763 -rice_maize,AGO,-0.08585424534976482 -rice_maize,ALB,0.0 +maize_soybean,VEN,-0.8716048002243042 +maize_soybean,VNM,-0.35146464407444 +maize_soybean,ZAF,-0.2168479636311531 +maize_soybean,ZMB,-0.5572142601013184 +maize_soybean,ZWE,1.0526694059371948 +rice_maize,AFG,-1.7495805025100708 +rice_maize,AGO,-2.388108730316162 +rice_maize,ALB,-0.0 rice_maize,ARG,7.5 -rice_maize,ARM,0.0 -rice_maize,AUS,7.5 -rice_maize,AZE,7.5 -rice_maize,BDI,-7.5 -rice_maize,BEN,-1.4199044704437256 -rice_maize,BGD,0.740846574306488 +rice_maize,ARM,-0.0 +rice_maize,AUS,-2.3311867713928223 +rice_maize,AZE,-3.8599894046783447 +rice_maize,BDI,-0.733100175857544 +rice_maize,BEN,-1.646537959575653 +rice_maize,BGD,0.32678547501564026 rice_maize,BGR,7.5 -rice_maize,BLZ,-2.2126665115356445 +rice_maize,BLZ,-2.670062303543091 rice_maize,BOL,7.5 -rice_maize,BRA,0.3613024950027466 -rice_maize,BTN,0.7664981931447983 -rice_maize,BWA,0.0 -rice_maize,CAF,-2.2831876277923584 -rice_maize,CAN,0.0 -rice_maize,CHL,-0.8489557802677155 -rice_maize,CHN,-2.4107013940811157 -rice_maize,CIV,-0.6918438673019409 -rice_maize,CMR,-1.4365019798278809 -rice_maize,COD,-2.40900456905365 -rice_maize,COL,-1.4636902809143066 -rice_maize,COM,-2.2929179668426514 +rice_maize,BRA,-0.29058557748794556 +rice_maize,BTN,-0.6720845103263855 +rice_maize,BWA,-0.0 +rice_maize,CAF,-2.3995039463043213 +rice_maize,CAN,-0.0 +rice_maize,CHL,-1.947529673576355 +rice_maize,CHN,-3.307382345199585 +rice_maize,CIV,-0.9656861424446106 +rice_maize,CMR,7.5 +rice_maize,COD,-2.518190383911133 +rice_maize,COL,-2.0998793840408325 +rice_maize,COM,-2.4091944694519043 rice_maize,CUB,7.5 -rice_maize,DOM,-1.4875823259353638 +rice_maize,DOM,2.308054804801941 rice_maize,DZA,7.5 -rice_maize,ECU,-0.15525095164775848 -rice_maize,EGY,-1.4274488687515259 +rice_maize,ECU,-0.8900418877601624 +rice_maize,EGY,-3.1733345985412598 rice_maize,ERI,7.5 -rice_maize,ESP,-0.6584328413009644 +rice_maize,ESP,-3.5793111324310303 rice_maize,ETH,7.5 rice_maize,FRA,7.5 rice_maize,GEO,7.5 -rice_maize,GHA,-0.7420739531517029 -rice_maize,GIN,-0.6635527610778809 -rice_maize,GNB,-7.5 -rice_maize,GRC,-1.0973598062992096 -rice_maize,GTM,2.292964458465576 -rice_maize,HND,-1.5444250106811523 -rice_maize,HTI,-2.014344811439514 +rice_maize,GHA,-1.031973958015442 +rice_maize,GIN,-0.7264726459980011 +rice_maize,GNB,-1.6693933010101318 +rice_maize,GRC,-3.095834970474243 +rice_maize,GTM,-2.2746835947036743 +rice_maize,HND,-1.8321233987808228 +rice_maize,HTI,-2.2494877576828003 rice_maize,HUN,7.5 -rice_maize,IDN,0.0 -rice_maize,IND,0.05001901835203171 -rice_maize,IRN,-3.5843350887298584 -rice_maize,IRQ,-1.9353299736976624 -rice_maize,ITA,-2.7837042808532715 -rice_maize,KAZ,0.8348073363304138 -rice_maize,KEN,-3.0181379318237305 -rice_maize,KGZ,-2.7912529706954956 -rice_maize,KOR,-6.391165018081665 -rice_maize,LAO,-0.9558684229850769 -rice_maize,LKA,-0.9154897928237915 -rice_maize,LSO,0.0 -rice_maize,MAR,-0.029556401073932648 +rice_maize,IDN,-0.0 +rice_maize,IND,-1.4378907680511475 +rice_maize,IRN,-4.248165130615234 +rice_maize,IRQ,-2.43397319316864 +rice_maize,ITA,-3.4090006351470947 +rice_maize,KAZ,-1.0813347697257996 +rice_maize,KEN,-1.3619197010993958 +rice_maize,KGZ,-3.5008654594421387 +rice_maize,KOR,-6.912171363830566 +rice_maize,LAO,-1.7018980979919434 +rice_maize,LKA,-1.307216763496399 +rice_maize,LSO,-0.0 +rice_maize,MAR,2.597246766090393 rice_maize,MDA,7.5 -rice_maize,MDG,-1.0697451829910278 -rice_maize,MEX,4.226221084594727 +rice_maize,MDG,-1.279721975326538 +rice_maize,MEX,-0.6952770352363586 rice_maize,MKD,7.5 -rice_maize,MLI,-3.798837773501873 -rice_maize,MMR,-1.661135196685791 -rice_maize,MOZ,-0.5322675704956055 +rice_maize,MLI,-0.6271843314170837 +rice_maize,MMR,-1.670688509941101 +rice_maize,MOZ,-0.5146883130073547 rice_maize,MUS,7.5 -rice_maize,MWI,-4.355122685432434 +rice_maize,MWI,-1.3178186416625977 rice_maize,NAM,7.5 -rice_maize,NGA,-0.42868663370609283 -rice_maize,NIC,-0.6727326214313507 -rice_maize,NPL,-0.8327913284301758 -rice_maize,PAK,-0.17984464764595032 -rice_maize,PER,-4.246907711029053 -rice_maize,PHL,-0.6145907342433929 +rice_maize,NGA,-0.7409452497959137 +rice_maize,NIC,-0.0 +rice_maize,NPL,-0.5504612028598785 +rice_maize,PAK,-1.49882173538208 +rice_maize,PER,-3.9759881496429443 +rice_maize,PHL,-1.1747495532035828 rice_maize,PRI,7.5 -rice_maize,PRT,3.393346428871155 -rice_maize,PRY,-0.28299304842948914 -rice_maize,ROU,3.283622592687607 +rice_maize,PRT,2.9210872650146484 +rice_maize,PRY,-1.1517144441604614 +rice_maize,ROU,-1.4203614592552185 rice_maize,RUS,7.5 -rice_maize,RWA,-1.6551365852355957 +rice_maize,RWA,-2.0939033031463623 rice_maize,SAU,7.5 -rice_maize,SDN,-2.432253122329712 +rice_maize,SDN,-2.53087055683136 rice_maize,SEN,7.5 -rice_maize,SLE,3.665683776140213 -rice_maize,SLV,-3.3491971492767334 -rice_maize,SOM,-7.033926963806152 -rice_maize,SSD,-2.2848644256591797 -rice_maize,SVK,0.0 -rice_maize,SVN,0.0 +rice_maize,SLE,7.5 +rice_maize,SLV,-3.1758580207824707 +rice_maize,SOM,-0.5762790441513062 +rice_maize,SSD,-2.371057868003845 +rice_maize,SVK,-0.0 +rice_maize,SVN,-0.0 rice_maize,SWZ,7.5 -rice_maize,SYR,0.0741366297006607 -rice_maize,TCD,-0.23063230514526367 -rice_maize,TGO,-0.17723476886749268 -rice_maize,THA,-0.6269423365592957 -rice_maize,TJK,-2.3703906536102295 -rice_maize,TKM,-0.8995541632175446 -rice_maize,TLS,-1.247755527496338 -rice_maize,TUN,0.0 -rice_maize,TUR,-2.077794075012207 -rice_maize,TZA,0.0 -rice_maize,UGA,-2.147894024848938 +rice_maize,SYR,-0.661085844039917 +rice_maize,TCD,-0.541948676109314 +rice_maize,TGO,-0.4622090458869934 +rice_maize,THA,-0.989659309387207 +rice_maize,TJK,-3.904982805252075 +rice_maize,TKM,-1.3028086423873901 +rice_maize,TLS,-1.3998390436172485 +rice_maize,TUN,-0.0 +rice_maize,TUR,-3.013861298561096 +rice_maize,TZA,-0.08381067216396332 +rice_maize,UGA,-2.3060479164123535 rice_maize,UKR,7.5 -rice_maize,URY,0.1967543587088585 -rice_maize,USA,7.5 -rice_maize,UZB,-5.961887836456299 -rice_maize,VEN,-0.756818950176239 -rice_maize,VNM,-0.7148265242576599 -rice_maize,YEM,0.0 -rice_maize,ZAF,4.899500370025635 -rice_maize,ZMB,-7.5 +rice_maize,URY,-0.8209086060523987 +rice_maize,USA,-1.962952733039856 +rice_maize,UZB,-6.036947727203369 +rice_maize,VEN,-1.313603401184082 +rice_maize,VNM,-1.380617380142212 +rice_maize,YEM,-0.0 +rice_maize,ZAF,-1.4254977107048035 +rice_maize,ZMB,-1.1254773139953613 rice_maize,ZWE,7.5 -rice_wheat,AFG,-0.9810315072536469 -rice_wheat,AGO,4.118537247180939 -rice_wheat,ALB,0.0 +rice_wheat,AFG,-1.3534507155418396 +rice_wheat,AGO,7.5 +rice_wheat,ALB,-0.0 rice_wheat,ARG,7.5 -rice_wheat,ARM,0.0 -rice_wheat,AUS,-7.5 -rice_wheat,AZE,7.5 -rice_wheat,BGD,-0.22736575454473495 -rice_wheat,BGR,7.5 +rice_wheat,ARM,-0.0 +rice_wheat,AUS,-2.1173155307769775 +rice_wheat,AZE,-3.3311681747436523 +rice_wheat,BGD,-0.760441780090332 +rice_wheat,BGR,2.800064980983734 rice_wheat,BOL,7.5 rice_wheat,BRA,7.5 -rice_wheat,BTN,7.5 -rice_wheat,BWA,0.0 -rice_wheat,CAN,0.0 -rice_wheat,CHL,-0.5391653776168823 -rice_wheat,CHN,-2.510178327560425 -rice_wheat,CMR,-1.4577618837356567 -rice_wheat,COD,-2.1262660026550293 +rice_wheat,BTN,-0.25281507743056864 +rice_wheat,BWA,-0.0 +rice_wheat,CAN,-0.0 +rice_wheat,CHL,-1.4021535515785217 +rice_wheat,CHN,-3.20658016204834 +rice_wheat,CMR,-1.7350846529006958 +rice_wheat,COD,-2.2580342292785645 rice_wheat,DZA,7.5 -rice_wheat,ECU,-1.2690553665161133 -rice_wheat,EGY,-0.8356800675392151 -rice_wheat,ESP,-0.4351092576980591 +rice_wheat,ECU,-1.4204405546188354 +rice_wheat,EGY,-2.800849676132202 +rice_wheat,ESP,-2.255333185195923 rice_wheat,ETH,7.5 rice_wheat,FRA,7.5 rice_wheat,GEO,7.5 -rice_wheat,GRC,-0.21108395606279373 -rice_wheat,GTM,0.0 -rice_wheat,HND,-1.8997968435287476 +rice_wheat,GRC,-1.6955810189247131 +rice_wheat,GTM,7.5 +rice_wheat,HND,-2.086721658706665 rice_wheat,HUN,7.5 -rice_wheat,IND,1.5363070368766785 -rice_wheat,IRN,-3.057201385498047 -rice_wheat,IRQ,-1.7608040571212769 -rice_wheat,ITA,-2.7929333448410034 -rice_wheat,JPN,-1.3070594668388367 -rice_wheat,KAZ,0.025521814823150635 -rice_wheat,KGZ,-2.949662446975708 -rice_wheat,KOR,7.5 -rice_wheat,LAO,0.0 -rice_wheat,LSO,0.0 -rice_wheat,MAR,-0.44990822672843933 +rice_wheat,IND,-1.2840489149093628 +rice_wheat,IRN,-3.555416226387024 +rice_wheat,IRQ,-2.129554271697998 +rice_wheat,ITA,-2.6914992332458496 +rice_wheat,JPN,-0.9766173958778381 +rice_wheat,KAZ,-1.0575806498527527 +rice_wheat,KGZ,-3.2925446033477783 +rice_wheat,KOR,-6.724577903747559 +rice_wheat,LAO,-0.0 +rice_wheat,LSO,-0.0 +rice_wheat,MAR,-2.4409526586532593 rice_wheat,MDA,7.5 -rice_wheat,MDG,-0.778467059135437 +rice_wheat,MDG,-0.9923009872436523 rice_wheat,MEX,7.5 rice_wheat,MKD,7.5 -rice_wheat,MLI,0.17528533935546875 -rice_wheat,MMR,-1.8721712827682495 +rice_wheat,MLI,-0.5925939083099365 +rice_wheat,MMR,-1.849918246269226 rice_wheat,MOZ,7.5 rice_wheat,MRT,7.5 -rice_wheat,MWI,7.5 -rice_wheat,NAM,7.5 -rice_wheat,NER,-7.5 -rice_wheat,NGA,-7.5 -rice_wheat,NPL,3.39884352684021 -rice_wheat,PAK,-0.5648182034492493 -rice_wheat,PRT,-0.8216572403907776 -rice_wheat,PRY,-0.3582548648118973 +rice_wheat,MWI,2.8602685928344727 +rice_wheat,NAM,2.656647562980652 +rice_wheat,NER,-1.4336342811584473 +rice_wheat,NGA,-0.8298945426940918 +rice_wheat,NPL,-0.7108609080314636 +rice_wheat,PAK,-1.4355943202972412 +rice_wheat,PRT,-1.3605966567993164 +rice_wheat,PRY,-0.9652633965015411 rice_wheat,ROU,7.5 rice_wheat,RUS,7.5 -rice_wheat,RWA,-1.8426926136016846 -rice_wheat,SDN,-7.5 -rice_wheat,SOM,-7.5 -rice_wheat,SVK,0.0 -rice_wheat,SVN,0.0 +rice_wheat,RWA,-2.2284631729125977 +rice_wheat,SDN,-1.6409181356430054 +rice_wheat,SOM,7.5 +rice_wheat,SVK,-0.0 +rice_wheat,SVN,-0.0 rice_wheat,SWZ,7.5 -rice_wheat,SYR,-0.2721511125564575 -rice_wheat,TCD,-6.479332208633423 +rice_wheat,SYR,-0.8439756631851196 +rice_wheat,TCD,-0.9173677265644073 rice_wheat,THA,7.5 -rice_wheat,TJK,-2.529783070087433 -rice_wheat,TKM,-1.085111916065216 -rice_wheat,TUN,0.0 -rice_wheat,TUR,-2.02057147026062 -rice_wheat,TZA,-0.14180435985326767 -rice_wheat,UGA,-1.8810851573944092 +rice_wheat,TJK,-3.6388649940490723 +rice_wheat,TKM,-1.3595415353775024 +rice_wheat,TUN,-0.0 +rice_wheat,TUR,-2.6333714723587036 +rice_wheat,TZA,-0.5026793479919434 +rice_wheat,UGA,-2.0544402599334717 rice_wheat,UKR,7.5 -rice_wheat,URY,0.2689153738319874 -rice_wheat,USA,7.5 -rice_wheat,UZB,-5.035796880722046 +rice_wheat,URY,-0.6990396976470947 +rice_wheat,USA,-1.5776442289352417 +rice_wheat,UZB,-4.868905544281006 rice_wheat,VEN,7.5 -rice_wheat,VNM,-1.0420973896980286 -rice_wheat,ZAF,4.236782073974609 -rice_wheat,ZMB,-7.5 +rice_wheat,VNM,-1.5102684497833252 +rice_wheat,ZAF,-1.5638744831085205 +rice_wheat,ZMB,-0.8846746683120728 rice_wheat,ZWE,7.5 triple_rice,AFG,7.5 triple_rice,AGO,7.5 @@ -496,13 +497,13 @@ triple_rice,AUS,7.5 triple_rice,BDI,7.5 triple_rice,BEN,7.5 triple_rice,BFA,7.5 -triple_rice,BGD,0.42879411578178406 +triple_rice,BGD,-0.6115118265151978 triple_rice,BLZ,7.5 triple_rice,BOL,7.5 triple_rice,BRA,7.5 triple_rice,BRN,7.5 -triple_rice,BTN,-2.9541730880737305 -triple_rice,BWA,0.0 +triple_rice,BTN,-2.8461802005767822 +triple_rice,BWA,-0.0 triple_rice,CHN,7.5 triple_rice,CIV,7.5 triple_rice,CMR,7.5 @@ -526,8 +527,8 @@ triple_rice,GMB,7.5 triple_rice,GNB,7.5 triple_rice,GNQ,7.5 triple_rice,GRC,7.5 -triple_rice,GTM,7.5 -triple_rice,GUF,0.0 +triple_rice,GTM,1.5453910827636719 +triple_rice,GUF,7.5 triple_rice,GUY,7.5 triple_rice,HND,7.5 triple_rice,HTI,7.5 @@ -537,7 +538,7 @@ triple_rice,IRN,7.5 triple_rice,IRQ,7.5 triple_rice,ITA,7.5 triple_rice,KEN,7.5 -triple_rice,KHM,-7.5 +triple_rice,KHM,-3.954114317893982 triple_rice,LAO,7.5 triple_rice,LBR,7.5 triple_rice,LKA,7.5 @@ -567,7 +568,7 @@ triple_rice,RWA,7.5 triple_rice,SAU,7.5 triple_rice,SDN,7.5 triple_rice,SEN,7.5 -triple_rice,SLB,-4.206660032272339 +triple_rice,SLB,-4.486171245574951 triple_rice,SLE,7.5 triple_rice,SLV,7.5 triple_rice,SOM,7.5 @@ -577,7 +578,7 @@ triple_rice,SWZ,7.5 triple_rice,TCD,7.5 triple_rice,TGO,7.5 triple_rice,THA,7.5 -triple_rice,TLS,7.5 +triple_rice,TLS,1.243781328201294 triple_rice,TTO,7.5 triple_rice,TUR,7.5 triple_rice,TWN,7.5 @@ -586,204 +587,204 @@ triple_rice,UGA,7.5 triple_rice,URY,7.5 triple_rice,USA,7.5 triple_rice,VEN,7.5 -triple_rice,VNM,7.5 -triple_rice,YEM,0.0 +triple_rice,VNM,2.532027840614319 +triple_rice,YEM,-0.0 triple_rice,ZAF,7.5 triple_rice,ZMB,7.5 triple_rice,ZWE,7.5 -wheat_maize,AFG,-0.8690876364707947 -wheat_maize,AGO,0.059513647109270096 +wheat_maize,AFG,-1.0099253356456757 +wheat_maize,AGO,-1.581254243850708 wheat_maize,ALB,7.5 wheat_maize,ARE,7.5 wheat_maize,ARG,7.5 -wheat_maize,ARM,0.2058352828025818 -wheat_maize,AUS,0.9481626749038696 +wheat_maize,ARM,-0.29412639141082764 +wheat_maize,AUS,0.04519475996494293 wheat_maize,AUT,7.5 -wheat_maize,AZE,0.2792627140879631 -wheat_maize,BDI,-1.0171025395393372 +wheat_maize,AZE,-0.8957000374794006 +wheat_maize,BDI,-1.1493258476257324 wheat_maize,BEL,7.5 -wheat_maize,BGD,0.06772636622190475 +wheat_maize,BGD,-0.15233726799488068 wheat_maize,BGR,7.5 wheat_maize,BIH,7.5 -wheat_maize,BOL,7.5 -wheat_maize,BRA,0.03994758613407612 -wheat_maize,BTN,1.6954857110977173 -wheat_maize,BWA,3.9140300303697586 +wheat_maize,BOL,-0.5905634164810181 +wheat_maize,BRA,-0.34408819675445557 +wheat_maize,BTN,1.08690744638443 +wheat_maize,BWA,-0.2176012247800827 wheat_maize,CAN,7.5 wheat_maize,CHE,7.5 -wheat_maize,CHL,-0.5218610167503357 -wheat_maize,CHN,-1.9780700206756592 -wheat_maize,CMR,-0.5570536255836487 -wheat_maize,COD,-1.4385384321212769 -wheat_maize,COL,-0.6037926077842712 +wheat_maize,CHL,-1.643505871295929 +wheat_maize,CHN,-2.670019745826721 +wheat_maize,CMR,-0.944013923406601 +wheat_maize,COD,-1.5697588920593262 +wheat_maize,COL,-1.1596239805221558 wheat_maize,CZE,7.5 wheat_maize,DEU,7.5 wheat_maize,DZA,7.5 -wheat_maize,ECU,-1.0156046152114868 -wheat_maize,EGY,-0.5802799463272095 -wheat_maize,ERI,-1.3752935528755188 -wheat_maize,ESP,0.25326231122016907 -wheat_maize,ETH,-0.21749646216630936 +wheat_maize,ECU,-1.3109267950057983 +wheat_maize,EGY,-2.7034671306610107 +wheat_maize,ERI,-1.5133994221687317 +wheat_maize,ESP,-0.6335636377334595 +wheat_maize,ETH,-0.5352818369865417 wheat_maize,FRA,7.5 -wheat_maize,GEO,-0.05994178354740143 -wheat_maize,GRC,-0.5499691963195801 -wheat_maize,GTM,6.511103868484497 -wheat_maize,HND,-1.0363565683364868 +wheat_maize,GEO,-0.05703403800725937 +wheat_maize,GRC,2.745781183242798 +wheat_maize,GTM,3.237878978252411 +wheat_maize,HND,-1.2028067708015442 wheat_maize,HRV,7.5 -wheat_maize,HUN,7.5 -wheat_maize,IND,-0.4139341861009598 -wheat_maize,IRN,-0.8348695039749146 -wheat_maize,IRQ,-0.9702644348144531 -wheat_maize,ISR,-6.798701763153076 -wheat_maize,ITA,0.3427973538637161 -wheat_maize,JOR,0.9669528603553772 -wheat_maize,KAZ,0.5049141943454742 -wheat_maize,KEN,-0.658938467502594 -wheat_maize,KGZ,0.002808114979416132 -wheat_maize,KOR,0.18171603977680206 -wheat_maize,LAO,0.0 -wheat_maize,LBN,-0.3543650656938553 +wheat_maize,HUN,-0.4037407413125038 +wheat_maize,IND,-0.8158817291259766 +wheat_maize,IRN,-1.3398287892341614 +wheat_maize,IRQ,-1.4171549081802368 +wheat_maize,ISR,-6.781064987182617 +wheat_maize,ITA,3.0711528062820435 +wheat_maize,JOR,-0.8906932771205902 +wheat_maize,KAZ,-0.7657935619354248 +wheat_maize,KEN,-0.8532283902168274 +wheat_maize,KGZ,-0.5902541279792786 +wheat_maize,KOR,-0.40849533677101135 +wheat_maize,LAO,-0.0 +wheat_maize,LBN,-0.7102650701999664 wheat_maize,LBY,7.5 -wheat_maize,LSO,0.15696971118450165 +wheat_maize,LSO,-0.0717716682702303 wheat_maize,LUX,7.5 -wheat_maize,MAR,-0.12357106804847717 +wheat_maize,MAR,-0.6269045472145081 wheat_maize,MDA,7.5 -wheat_maize,MDG,-0.9451302886009216 -wheat_maize,MEX,3.6584975719451904 +wheat_maize,MDG,-1.2473762035369873 +wheat_maize,MEX,-1.2082997560501099 wheat_maize,MKD,7.5 -wheat_maize,MMR,-0.9334716796875 +wheat_maize,MMR,-0.6819483935832977 wheat_maize,MNE,7.5 -wheat_maize,MOZ,-0.14417719841003418 -wheat_maize,MWI,7.5 -wheat_maize,NAM,-5.4132819175720215 +wheat_maize,MOZ,-0.32281121611595154 +wheat_maize,MWI,3.352478265762329 +wheat_maize,NAM,-1.128983199596405 wheat_maize,NIC,7.5 wheat_maize,NLD,7.5 -wheat_maize,NPL,0.5423147678375244 -wheat_maize,NZL,-0.7800803184509277 -wheat_maize,OMN,-5.889230728149414 -wheat_maize,PAK,0.21099938452243805 -wheat_maize,PER,-1.0934141874313354 +wheat_maize,NPL,-0.6020831167697906 +wheat_maize,NZL,-1.8989965915679932 +wheat_maize,OMN,-4.073334693908691 +wheat_maize,PAK,-0.6937783062458038 +wheat_maize,PER,-1.516123354434967 wheat_maize,POL,7.5 -wheat_maize,PRT,7.5 -wheat_maize,PRY,-0.10249026119709015 -wheat_maize,PSE,2.3718225955963135 +wheat_maize,PRT,-0.5709085464477539 +wheat_maize,PRY,-0.5105574131011963 +wheat_maize,PSE,1.1821171045303345 wheat_maize,ROU,7.5 wheat_maize,RUS,7.5 -wheat_maize,RWA,-0.4008308947086334 -wheat_maize,SAU,-4.490570783615112 -wheat_maize,SLV,0.0 -wheat_maize,SOM,-7.5 +wheat_maize,RWA,-0.5994908809661865 +wheat_maize,SAU,-5.082570314407349 +wheat_maize,SLV,-0.0 +wheat_maize,SOM,-0.052111005410552025 wheat_maize,SRB,7.5 wheat_maize,SSD,7.5 wheat_maize,SVK,7.5 wheat_maize,SVN,7.5 -wheat_maize,SWZ,-1.1068166494369507 -wheat_maize,SYR,-0.2270970493555069 +wheat_maize,SWZ,-1.2787785530090332 +wheat_maize,SYR,-0.6548186838626862 wheat_maize,THA,7.5 -wheat_maize,TJK,0.031677063554525375 -wheat_maize,TKM,-0.7436801791191101 -wheat_maize,TUN,0.0 -wheat_maize,TUR,0.6552011966705322 -wheat_maize,TWN,0.6043800711631775 -wheat_maize,TZA,-0.1421385556459427 -wheat_maize,UGA,-1.0860267877578735 +wheat_maize,TJK,-0.8432182669639587 +wheat_maize,TKM,-0.8559079766273499 +wheat_maize,TUN,-0.0 +wheat_maize,TUR,-0.11955956742167473 +wheat_maize,TWN,-0.1585310846567154 +wheat_maize,TZA,-0.36638346314430237 +wheat_maize,UGA,-1.3114354014396667 wheat_maize,UKR,7.5 -wheat_maize,URY,0.02791692316532135 -wheat_maize,USA,0.012668413110077381 -wheat_maize,UZB,-1.54970121383667 +wheat_maize,URY,-0.5808399766683578 +wheat_maize,USA,-0.9632321894168854 +wheat_maize,UZB,-2.073707938194275 wheat_maize,VEN,7.5 -wheat_maize,VNM,0.15827590227127075 -wheat_maize,YEM,-7.5 -wheat_maize,ZAF,4.614251613616943 -wheat_maize,ZMB,-7.169391393661499 -wheat_maize,ZWE,6.538937091827393 -wheat_soybean,AFG,0.39539945870637894 -wheat_soybean,AGO,-0.020095903397304937 +wheat_maize,VNM,-0.5880140662193298 +wheat_maize,YEM,-1.7302067875862122 +wheat_maize,ZAF,-0.47737446427345276 +wheat_maize,ZMB,0.2719368040561676 +wheat_maize,ZWE,7.5 +wheat_soybean,AFG,0.3891846835613251 +wheat_soybean,AGO,-1.2161225080490112 wheat_soybean,ALB,7.5 wheat_soybean,ARG,7.5 wheat_soybean,ARM,7.5 -wheat_soybean,AUS,1.477770447731018 -wheat_soybean,AUT,7.5 -wheat_soybean,AZE,7.5 -wheat_soybean,BDI,-1.4418007731437683 +wheat_soybean,AUS,1.3267191648483276 +wheat_soybean,AUT,4.29094934463501 +wheat_soybean,AZE,1.0549312829971313 +wheat_soybean,BDI,-1.4991952776908875 wheat_soybean,BEL,7.5 -wheat_soybean,BGD,3.67583816498518 +wheat_soybean,BGD,3.5746075958013535 wheat_soybean,BGR,7.5 wheat_soybean,BIH,7.5 wheat_soybean,BOL,7.5 -wheat_soybean,BRA,0.13935944437980652 +wheat_soybean,BRA,0.001530438064946793 wheat_soybean,BTN,7.5 wheat_soybean,BWA,7.5 wheat_soybean,CAN,7.5 wheat_soybean,CHE,7.5 -wheat_soybean,CHN,-1.2130309343338013 -wheat_soybean,CMR,-0.3349729925394058 -wheat_soybean,COD,-1.205998718738556 +wheat_soybean,CHN,-1.5545575022697449 +wheat_soybean,CMR,-0.564705491065979 +wheat_soybean,COD,-1.3115870356559753 wheat_soybean,CZE,7.5 wheat_soybean,DEU,7.5 -wheat_soybean,DZA,0.0 -wheat_soybean,ECU,-0.5063061118125916 -wheat_soybean,EGY,-1.0885406732559204 -wheat_soybean,ESP,7.5 -wheat_soybean,ETH,-0.6716628670692444 +wheat_soybean,DZA,-0.0 +wheat_soybean,ECU,-0.5990945100784302 +wheat_soybean,EGY,-1.7183692455291748 +wheat_soybean,ESP,0.30121442675590515 +wheat_soybean,ETH,-0.8096926808357239 wheat_soybean,FRA,7.5 -wheat_soybean,GEO,0.2682209312915802 -wheat_soybean,GRC,3.8156702294945717 -wheat_soybean,GTM,2.7479088306427 -wheat_soybean,HND,-0.37444475293159485 +wheat_soybean,GEO,0.2604876160621643 +wheat_soybean,GRC,3.496550589799881 +wheat_soybean,GTM,3.9158102571964264 +wheat_soybean,HND,-0.6383743733167648 wheat_soybean,HRV,7.5 -wheat_soybean,HUN,7.5 -wheat_soybean,IND,2.4143319129943848 -wheat_soybean,IRN,-0.02305993204936385 -wheat_soybean,IRQ,-0.21165601909160614 +wheat_soybean,HUN,0.4032883383333683 +wheat_soybean,IND,-0.48902812600135803 +wheat_soybean,IRN,-0.008083773776888847 +wheat_soybean,IRQ,-0.4151475913822651 wheat_soybean,ISR,7.5 wheat_soybean,ITA,7.5 -wheat_soybean,JOR,0.0 -wheat_soybean,JPN,1.5681341886520386 +wheat_soybean,JOR,-0.0 +wheat_soybean,JPN,1.6944010257720947 wheat_soybean,KAZ,7.5 -wheat_soybean,KEN,-0.6364563405513763 -wheat_soybean,KGZ,3.7303013261407614 -wheat_soybean,KOR,-0.19438032805919647 -wheat_soybean,LAO,0.0 +wheat_soybean,KEN,-0.7492762506008148 +wheat_soybean,KGZ,-0.16147242486476898 +wheat_soybean,KOR,3.5074272453784943 +wheat_soybean,LAO,-0.0 wheat_soybean,LBN,7.5 wheat_soybean,LSO,7.5 wheat_soybean,LUX,7.5 -wheat_soybean,MAR,-0.4596904516220093 +wheat_soybean,MAR,-1.6725507974624634 wheat_soybean,MDA,7.5 -wheat_soybean,MEX,7.5 +wheat_soybean,MEX,-0.21033122390508652 wheat_soybean,MKD,7.5 -wheat_soybean,MMR,-0.2157789170742035 +wheat_soybean,MMR,3.9158369600772858 wheat_soybean,MNE,7.5 wheat_soybean,MOZ,7.5 -wheat_soybean,MWI,7.5 +wheat_soybean,MWI,3.3369496166706085 wheat_soybean,NGA,7.5 wheat_soybean,NLD,7.5 wheat_soybean,NPL,7.5 -wheat_soybean,PAK,-0.12195841688662767 -wheat_soybean,PER,-0.6334656476974487 +wheat_soybean,PAK,-0.49255894124507904 +wheat_soybean,PER,-0.7324284911155701 wheat_soybean,POL,7.5 wheat_soybean,PRT,7.5 -wheat_soybean,PRY,0.1659405753016472 +wheat_soybean,PRY,-0.1876545250415802 wheat_soybean,ROU,7.5 wheat_soybean,RUS,7.5 -wheat_soybean,RWA,-0.39666540920734406 +wheat_soybean,RWA,-0.4722822606563568 wheat_soybean,SRB,7.5 wheat_soybean,SVK,7.5 wheat_soybean,SVN,7.5 -wheat_soybean,SWZ,-0.33505555987358093 +wheat_soybean,SWZ,-0.5214117765426636 wheat_soybean,SYR,7.5 wheat_soybean,THA,7.5 wheat_soybean,TJK,7.5 -wheat_soybean,TKM,0.8133601248264313 +wheat_soybean,TKM,4.336043536663055 wheat_soybean,TUR,7.5 -wheat_soybean,TWN,-0.14883071184158325 -wheat_soybean,TZA,-0.2885752320289612 -wheat_soybean,UGA,-1.077957034111023 +wheat_soybean,TWN,-0.3851216435432434 +wheat_soybean,TZA,-0.43253564834594727 +wheat_soybean,UGA,-1.1988670825958252 wheat_soybean,UKR,7.5 -wheat_soybean,URY,-0.003528994508087635 -wheat_soybean,USA,0.34375500679016113 +wheat_soybean,URY,0.18366959691047668 +wheat_soybean,USA,0.02019089087843895 wheat_soybean,UZB,7.5 -wheat_soybean,VNM,-0.3192991614341736 -wheat_soybean,ZAF,2.5342297554016113 -wheat_soybean,ZMB,5.222832202911377 -wheat_soybean,ZWE,4.768944978713989 +wheat_soybean,VNM,-0.6542010307312012 +wheat_soybean,ZAF,-0.4300876557826996 +wheat_soybean,ZMB,-0.37386277318000793 +wheat_soybean,ZWE,4.438183784484863 diff --git a/data/curated/calibration/gbd-anchored/provenance.yaml b/data/curated/calibration/gbd-anchored/provenance.yaml index 994504f3..cf8e4a1f 100644 --- a/data/curated/calibration/gbd-anchored/provenance.yaml +++ b/data/curated/calibration/gbd-anchored/provenance.yaml @@ -4,12 +4,12 @@ # tools/calibrate; do not edit by hand. # Licensing: see the annotation in REUSE.toml. base_config: config/gsa.yaml -generated_at: '2026-07-22T00:59:36+00:00' -git_commit: 548ef138989c4a0ed28a90adeb0e4626b044a8ed +generated_at: '2026-07-22T19:57:11+00:00' +git_commit: 81a69806335b9eeba703c84240982fd8ba8d131d source: gbd-anchored structural_config: aggregation.irrigated_area_source: current - aggregation.regions.allow_cross_border: false + aggregation.regions.basin_scarcity_weight: 2.0 aggregation.regions.method: kmeans aggregation.regions.target_count: 750 aggregation.resource_class_quantiles: @@ -991,14 +991,27 @@ structural_config: numerics.min_grassland_yield_t_per_ha: 0.05 numerics.min_land_area_ha: 100 numerics.min_link_area_mha: 1.0e-06 + numerics.min_water_capacity_mm3: 1.0e-06 numerics.min_water_requirement_m3_per_ha: 0.1 optimal_taxes.enabled: false residues.max_feed_fraction: 0.3 residues.max_feed_fraction_by_region.Asia: 0.7 residues.max_feed_fraction_by_region.Southern Asia: 0.85 residues.max_feed_fraction_by_region.Sub-Saharan Africa: 0.7 - water.huang_reference_year: 2010 - water.supply_scenario: current_use + water.data.availability: aware + water.data.groundwater_trend_end: 2019 + water.data.groundwater_trend_start: 2000 + water.data.huang_reference_year: 2010 + water.data.surface_reference_end: 2019 + water.data.surface_reference_start: 1990 + water.irrigation.consumed_fraction: 0.58 + water.irrigation.eta_max: 5.0 + water.irrigation.eta_min: 0.2 + water.supply.groundwater: true + water.supply.groundwater_ceiling_factor: 3.0 + water.supply.pumping_cost_usd_per_m3: 0.04 + water.supply.scarcity_tiers: false + water.temporal_resolution: 1 weight_conversion.carcass_to_fresh.meat-cattle: 0.67 weight_conversion.carcass_to_fresh.meat-chicken: 0.6 weight_conversion.carcass_to_fresh.meat-pig: 0.73 From 0fab246bd785f6bc7bb2895a560be8a141178a05 Mon Sep 17 00:00:00 2001 From: Koen van Greevenbroek Date: Wed, 22 Jul 2026 13:18:04 -0700 Subject: [PATCH 09/12] fix: tolerate incomplete cost calibration under accepted provenance mismatch The regenerated cost artefacts are the first fit on the merged basin-aware region map, whose review fixes shifted the 750-region geometry: banana:CHL and sugarcane:CHL no longer build any link there (no actual-yield coverage on the new regions), so the artefact carries no rows for them. The 200-region test config still builds those links with a positive baseline, and the missing-correction check made every test build fail. The old artefact only passed by luck -- it predates the region-map fixes. Cross-resolution coverage gaps are inherent to consuming an artefact fit on a different region map, which is exactly what calibration.accept_provenance_mismatch declares. Such configs now get a warning and zero corrections for the missing pairs; provenance-matched configs keep the hard staleness error. --- tests/test_crop_costs.py | 17 +++++++++++++++++ workflow/scripts/build_model.py | 8 ++++++++ workflow/scripts/build_model/crops.py | 15 +++++++++++++-- 3 files changed, 38 insertions(+), 2 deletions(-) diff --git a/tests/test_crop_costs.py b/tests/test_crop_costs.py index 73aa2b1b..ebe83d06 100644 --- a/tests/test_crop_costs.py +++ b/tests/test_crop_costs.py @@ -76,6 +76,7 @@ def _add_rice_wheat_multi_link( baseline_combination="rice_wheat", potential_region="regionA", cycle_crops=("wetland-rice", "wheat"), + require_complete_cost_calibration=True, ): """Build a single rice-wheat multi-cropping link and return the network.""" n = pypsa.Network() @@ -157,6 +158,7 @@ def _add_rice_wheat_multi_link( combinations={"rice_wheat": {"crops": ["wetland-rice", "wheat"]}}, baseline_area=baseline_area, multi_crop_cost_calibration=multi_crop_cost_calibration, + require_complete_cost_calibration=require_complete_cost_calibration, ) return n @@ -224,6 +226,21 @@ def test_multi_cropping_raises_on_stale_cost_calibration(): ) +def test_multi_cropping_stale_calibration_warns_when_not_required(): + """With accept_provenance_mismatch, a missing pair warns and takes zero.""" + n = _add_rice_wheat_multi_link( + baseline_ha=500_000.0, + multi_crop_cost_calibration=pd.Series({("maize_soybean", "USA"): 1.0}), + require_complete_cost_calibration=False, + ) + + links = n.links.static[n.links.static["carrier"] == "crop_production_multi"] + assert len(links) == 1 + link = links.iloc[0] + assert float(link["bounded_penalty_bnusd_per_mha"]) == pytest.approx(0.0) + assert float(link["bounded_subsidy_bnusd_per_mha"]) == pytest.approx(0.0) + + def test_multi_cropping_zero_baseline_missing_correction_is_zero(): """A zero-baseline bundle absent from the artefact gets a zero correction.""" n = _add_rice_wheat_multi_link( diff --git a/workflow/scripts/build_model.py b/workflow/scripts/build_model.py index edc95471..3997c944 100644 --- a/workflow/scripts/build_model.py +++ b/workflow/scripts/build_model.py @@ -608,6 +608,12 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: ) * (1.0 - moisture_df["moisture_fraction"]) # Optional cost calibration corrections (crops, multi-crops, grassland, animals) + # Configs that accept a calibration provenance mismatch (test/tutorial + # grade) may build (crop, country) links the artefact's own build did not + # carry; the missing-correction check then warns instead of raising. + require_complete_cost_calibration = not bool( + snakemake.config["calibration"]["accept_provenance_mismatch"] + ) crop_cost_calibration = None multi_crop_cost_calibration = None grassland_cost_calibration = None @@ -997,6 +1003,7 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: water_periods=water_periods, irrigation_calendar=irrigation_calendar_df, cost_calibration=crop_cost_calibration, + require_complete_cost_calibration=require_complete_cost_calibration, min_yield_t_per_ha=min_crop_yield, seed_kg_dm_per_ha=seed_kg_dm_per_ha, crop_loss_multiplier=crop_loss_multiplier, @@ -1040,6 +1047,7 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None: baseline_area=multi_cropping_baseline_df, use_actual_production=use_actual_production, multi_crop_cost_calibration=multi_crop_cost_calibration, + require_complete_cost_calibration=require_complete_cost_calibration, ) # Clip sub-min_link_area_mha crop baselines before multi-crop reconciliation, # pinning, or growth-cap aggregation. Reconciliation can then conserve the diff --git a/workflow/scripts/build_model/crops.py b/workflow/scripts/build_model/crops.py index d4b84dd7..2910621c 100644 --- a/workflow/scripts/build_model/crops.py +++ b/workflow/scripts/build_model/crops.py @@ -233,6 +233,7 @@ def _apply_bounded_cost_calibration( *, label: str, key_fields: str, + require_complete: bool, ) -> None: """Store bounded cost-calibration corrections on crop production links. @@ -259,7 +260,10 @@ def _apply_bounded_cost_calibration( floor) receive a dual, so pairs with a zero baseline are legitimately absent from the artefact and correctly take a zero correction. A positive-baseline pair that is missing means the artefact is stale for the - current link set, and raises. + current link set and raises when ``require_complete`` -- except for configs + that declare ``calibration.accept_provenance_mismatch`` (test/tutorial + grade), whose region map may legitimately carry (crop, country) pairs the + artefact's build did not; those warn and take a zero correction. """ df["bounded_subsidy_bnusd_per_mha"] = 0.0 df["bounded_penalty_bnusd_per_mha"] = 0.0 @@ -278,12 +282,15 @@ def _apply_bounded_cost_calibration( if missing.any(): missing_keys = sorted({keys[i] for i in np.flatnonzero(missing)}) sample = ", ".join(":".join(map(str, k)) for k in missing_keys[:5]) - raise ValueError( + message = ( f"{label} cost calibration is missing {len(missing_keys)} " f"{key_fields} link(s) with a positive baseline area, e.g. {sample}. " "The calibration artefact is stale for the current link set; " "regenerate it with `tools/calibrate cost`." ) + if require_complete: + raise ValueError(message) + logger.warning("%s Proceeding with zero corrections for them.", message) corrections = corrections.fillna(0.0) pos = corrections > 0 @@ -339,6 +346,7 @@ def add_regional_crop_production_links( *, water_periods: int, irrigation_calendar: pd.DataFrame, + require_complete_cost_calibration: bool = True, cost_calibration: pd.Series | None = None, min_yield_t_per_ha: float, seed_kg_dm_per_ha: pd.Series, @@ -659,6 +667,7 @@ def add_regional_crop_production_links( cost_calibration, label="crop", key_fields="(crop, country)", + require_complete=require_complete_cost_calibration, ) keys = list( @@ -772,6 +781,7 @@ def add_multi_cropping_links( baseline_area: pd.DataFrame | None = None, use_actual_production: bool = False, multi_crop_cost_calibration: pd.Series | None = None, + require_complete_cost_calibration: bool = True, ) -> None: """Add multi-cropping production links with a vectorised workflow. @@ -1207,6 +1217,7 @@ def add_multi_cropping_links( multi_crop_cost_calibration, label="multi-crop", key_fields="(combination, country)", + require_complete=require_complete_cost_calibration, ) if use_actual_production: From 157a2476b33e7af4897a51de2d69e5e20b277fb9 Mon Sep 17 00:00:00 2001 From: Koen van Greevenbroek Date: Wed, 22 Jul 2026 13:37:18 -0700 Subject: [PATCH 10/12] fix: give the MIRCA calendar monthly grids a producing rule extract_mirca_os_subcrop_monthly only unpacked Rice2/Rice3, so the irrigated monthly grids build_mirca_crop_calendar needs had no producer in a clean checkout -- the CI dryrun failed with MissingInputException while local runs passed on manually extracted files. The rule now unpacks every calendar subcrop grid (single-sourced list shared with water.smk's input function) in the same single archive pass. --- workflow/rules/retrieve.smk | 62 ++++++++++++++++++++++++++++++------- workflow/rules/water.smk | 39 ++++++----------------- 2 files changed, 60 insertions(+), 41 deletions(-) diff --git a/workflow/rules/retrieve.smk b/workflow/rules/retrieve.smk index 880c8b52..65fedf02 100644 --- a/workflow/rules/retrieve.smk +++ b/workflow/rules/retrieve.smk @@ -823,12 +823,45 @@ MIRCA_OS_BAG_MEMBERS = { } MIRCA_OS_YEARS = (2000, 2005, 2010, 2015, 2020) with open("data/curated/mirca_os_crop_mapping.csv") as _mirca_mapping_file: - MIRCA_OS_BASE_CROPS = [ + _mirca_mapping_rows = list( + csv.DictReader(line for line in _mirca_mapping_file if not line.startswith("#")) + ) +MIRCA_OS_BASE_CROPS = [row["mirca_crop"].strip() for row in _mirca_mapping_rows] + +# Monthly-grid subcrop labels feeding the irrigated crop calendar: every +# GLADE-mapped base crop from the concordance plus the calendar-only +# supplement, expanded into MIRCA-OS's subcrop naming (Rice1/2/3, Wheat1/2, +# else the base label). Shared with water.smk's calendar input function so the +# extraction rule below provably produces what build_mirca_crop_calendar needs. +MIRCA_SUBCROP_CYCLES = {"Rice": 3, "Wheat": 2} +with open("data/curated/mirca_os_calendar_supplement.csv") as _mirca_supplement_file: + _mirca_calendar_bases = [ row["mirca_crop"].strip() - for row in csv.DictReader( - line for line in _mirca_mapping_file if not line.startswith("#") + for row in _mirca_mapping_rows + + list( + csv.DictReader( + line for line in _mirca_supplement_file if not line.startswith("#") + ) ) + if row["glade_crop"].strip() ] +MIRCA_OS_CALENDAR_SUBCROPS = sorted( + { + label + for base in _mirca_calendar_bases + for label in ( + [f"{base}{i}" for i in range(1, MIRCA_SUBCROP_CYCLES[base] + 1)] + if base in MIRCA_SUBCROP_CYCLES + else [base] + ) + } +) +# All monthly grid members one archive pass must yield: the irrigated calendar +# grids plus the repeated-rice grids (both systems) for multi-cropping. +_MIRCA_MONTHLY_MEMBERS = sorted( + {(subcrop, "ir") for subcrop in MIRCA_OS_CALENDAR_SUBCROPS} + | {(subcrop, ws) for subcrop in ("Rice2", "Rice3") for ws in ("ir", "rf")} +) rule download_mirca_os_archive: @@ -916,27 +949,32 @@ rule extract_mirca_os_footprint: rule extract_mirca_os_subcrop_monthly: - """Unpack the repeated-rice monthly grids for one year in one archive pass. + """Unpack all needed monthly growing-area grids for one year in one pass. The subcrop-resolved monthly product preserves subcrop identity - (Rice1/2/3, Wheat1/2, ...). Rice2/3 feed the multi-cropping derivation's - repeated-cycle detection (never crop timing). + (Rice1/2/3, Wheat1/2, ...). The irrigated grids of every calendar crop + feed build_mirca_crop_calendar; Rice2/3 (both systems) additionally feed + the multi-cropping derivation's repeated-cycle detection. """ input: rar="data/downloads/mirca_os/Monthly_Growing_Area_Grids.rar", output: expand( - "data/downloads/mirca_os/grids/monthly/MIRCA-OS_{subcrop}_{{year}}_{ws}.nc", - subcrop=("Rice2", "Rice3"), - ws=("ir", "rf"), + "data/downloads/mirca_os/grids/monthly/MIRCA-OS_{subcrop}_{year}_{ws}.nc", + zip, + subcrop=[m[0] for m in _MIRCA_MONTHLY_MEMBERS], + ws=[m[1] for m in _MIRCA_MONTHLY_MEMBERS], + allow_missing=True, ), wildcard_constraints: year="|".join(map(str, MIRCA_OS_YEARS)), params: member_globs=expand( - "*MIRCA-OS_{subcrop}_{{year}}_{ws}.nc", - subcrop=("Rice2", "Rice3"), - ws=("ir", "rf"), + "*MIRCA-OS_{subcrop}_{year}_{ws}.nc", + zip, + subcrop=[m[0] for m in _MIRCA_MONTHLY_MEMBERS], + ws=[m[1] for m in _MIRCA_MONTHLY_MEMBERS], + allow_missing=True, ), resources: runtime="30m", diff --git a/workflow/rules/water.smk b/workflow/rules/water.smk index 265d494a..d529b367 100644 --- a/workflow/rules/water.smk +++ b/workflow/rules/water.smk @@ -165,39 +165,20 @@ rule build_region_watergap: "../scripts/build_region_watergap.py" -# MIRCA-OS v2 crops with more than one irrigated sub-crop cycle in the monthly -# grids; every other base crop ships a single grid without a cycle digit. Used -# to expand each mapped base crop into its sub-crop grid filenames. -_MIRCA_SUBCROP_CYCLES = {"Rice": 3, "Wheat": 2} - - def mirca_calendar_grids(w): - """2015 irrigated monthly growing-area grids for every mapped MIRCA crop. + """2015 irrigated monthly growing-area grids for every calendar crop. - Reads the crop concordance plus the calendar-only supplement and expands - each non-dropped MIRCA base crop into its sub-crop grid(s) (``Rice1/2/3``, - ``Wheat1/2``, else the base label), keyed ``nc_{subcrop}`` for + One entry per subcrop label in ``MIRCA_OS_CALENDAR_SUBCROPS`` (the + concordance plus the calendar-only supplement, expanded to MIRCA's subcrop + naming in retrieve.smk), keyed ``nc_{subcrop}`` for ``build_mirca_crop_calendar``. """ - import pandas as pd - - mapping = pd.concat( - [ - pd.read_csv("data/curated/mirca_os_crop_mapping.csv", comment="#"), - pd.read_csv("data/curated/mirca_os_calendar_supplement.csv", comment="#"), - ], - ignore_index=True, - ) - mapping = mapping[mapping["glade_crop"].notna() & (mapping["glade_crop"] != "")] - grids = {} - for base in mapping["mirca_crop"].unique(): - n = _MIRCA_SUBCROP_CYCLES.get(base, 1) - labels = [f"{base}{i}" for i in range(1, n + 1)] if n > 1 else [base] - for label in labels: - grids[f"nc_{label}"] = ( - f"data/downloads/mirca_os/grids/monthly/MIRCA-OS_{label}_2015_ir.nc" - ) - return grids + return { + f"nc_{label}": ( + f"data/downloads/mirca_os/grids/monthly/MIRCA-OS_{label}_2015_ir.nc" + ) + for label in MIRCA_OS_CALENDAR_SUBCROPS + } # Observed irrigated crop calendar: per (region, crop) monthly water-demand From ac8ebe7e7bcd34da622fb4294165ea98aa4bb063 Mon Sep 17 00:00:00 2001 From: Koen van Greevenbroek Date: Wed, 22 Jul 2026 13:59:09 -0700 Subject: [PATCH 11/12] feat: characterise renewable water as one AWARE resource Resolves the open design questions from review: - Each basin's AWARE CF curve now spans the joint renewable envelope (WaterGAP surface delivery plus renewable groundwater) and is split at the basin's surface fraction: the lower slice is period-bound surface, the upper slice becomes annual per-region renewable-groundwater CF bands. This matches AWARE's own methodology (availability is basin discharge including baseflow; CF application is source-agnostic) and replaces the flat band at the region's scarcest surface CF, which saturated at the AWARE cutoff in 86% of regions and drifted with the temporal resolution. On the test build the renewable-GW CF now spans a real distribution (volume-weighted mean 35 vs uniformly ~100 before). - water.supply.groundwater is removed; groundwater is always part of the aware supply (constrain at solve time for mining-free systems), and the current_use source emits no bands -- its observed-withdrawal pool already contains groundwater, so additive bands double-counted it. - Irrigation's share of the groundwater-storage depletion trend is attributed by pirrusegw/ptotusegw (new WaterGAP download), so basins mined by municipal or industrial pumping no longer zero irrigation's renewable band. - A scarcity cap with nonrenewable_cf set is now a joint constraint charging each mined m3 CF-fold against the cap, mirroring pricing; the porosity warning remains for null-cf caps. - run_solve gates the groundwater levers on the availability source (water_availability param replaces water_groundwater). --- CHANGELOG.md | 36 +- config/default.yaml | 47 +- config/schemas/config.schema.yaml | 24 +- docs/configuration.rst | 3 +- docs/data_sources.rst | 2 +- docs/water.rst | 91 ++-- docs/workflow.rst | 2 +- tests/config/test.yaml | 3 +- tests/test_water_groundwater.py | 79 ++-- tests/test_water_surface.py | 165 +++++-- tests/test_water_temporal.py | 32 +- workflow/rules/analysis.smk | 2 +- workflow/rules/model.smk | 2 +- workflow/rules/retrieve.smk | 11 +- workflow/rules/water.smk | 18 +- workflow/scripts/build_model/irrigation.py | 4 +- .../scripts/build_model/primary_resources.py | 40 +- workflow/scripts/build_region_water_aware.py | 406 ++++++++++++------ workflow/scripts/build_region_watergap.py | 42 +- workflow/scripts/compose_water_supply.py | 156 +++---- workflow/scripts/solve_model/core.py | 86 ++-- workflow/scripts/solve_namespace.py | 2 +- 22 files changed, 814 insertions(+), 439 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index a8b5a4cd..2359aa98 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -118,18 +118,34 @@ introduce breaking changes to configuration and outputs. consequence worth stating plainly: at annual resolution the groundwater bands are nearly inert and reported depletion falls to near zero — an artefact of the resolution, not a finding.** Studies about water should raise it. -- Water supply fidelity is now two independent switches rather than a ladder: - `water.supply.scarcity_tiers` (convex AWARE scarcity tiers, default off — - each region-period pool is one flat availability cap) and - `water.supply.groundwater` (additive annual renewable and non-renewable - groundwater bands, default on). All four combinations are valid. Scarcity - pricing or capping now requires `scarcity_tiers` and raises otherwise, since - with collapsed tiers there is no scarcity signal to price. +- Surface water and renewable groundwater are characterised as **one AWARE + renewable resource**: each basin's CF curve spans the joint envelope + (WaterGAP surface delivery plus renewable groundwater) and is split at the + basin's surface fraction — the lower slice is period-bound surface, the + upper slice becomes annual per-region renewable-groundwater CF bands. This + replaces the earlier draft's flat renewable-groundwater band at the region's + scarcest surface CF, which saturated at the AWARE cutoff (CF 100) almost + everywhere and drifted with the temporal resolution. Groundwater is always + part of the aware supply (the `water.supply.groundwater` switch is removed; + cap mining at solve time via `groundwater_depletion.cap_mm3: 0` for a + mining-free system); the `current_use` source emits no groundwater bands, + since its observed-withdrawal pool already contains groundwater. Irrigation's + share of the groundwater-storage depletion trend is attributed by its share + of all-sector potential groundwater consumption (new WaterGAP `ptotusegw` + download), so basins mined by municipal or industrial pumping no longer + zero irrigation's renewable band. Water supply fidelity remains a single + switch, `water.supply.scarcity_tiers` (convex AWARE scarcity curves, default + off — each pool is one flat availability cap). Scarcity pricing or capping + requires it and raises otherwise, since with collapsed curves there is no + scarcity signal to price. - New optional solve-time levers, both off by default: `water_scarcity` (pricing and/or capping accumulated AWARE scarcity) and - `groundwater_depletion` (pricing and/or capping accumulated mining). Analysis - gains a `water_metrics` output with per-region withdrawal, scarcity, - renewable groundwater and depletion. + `groundwater_depletion` (pricing and/or capping accumulated mining). With + `water_scarcity.nonrenewable_cf` set, mined groundwater is charged at that + CF under scarcity pricing *and* counts CF-fold against a scarcity cap (a + joint constraint), so neither lever can be satisfied by free substitution + into fossil groundwater. Analysis gains a `water_metrics` output with + per-region withdrawal, scarcity, renewable groundwater and depletion. - Model regions are now built **basin-aware**: GADM provinces are first split along AWARE hydrological basin boundaries, and each country is partitioned into regions balancing geography against basin scarcity diff --git a/config/default.yaml b/config/default.yaml index 19c37e2c..cc3a5790 100644 --- a/config/default.yaml +++ b/config/default.yaml @@ -264,23 +264,23 @@ water: # at T=1 is an artefact of the resolution, not a finding. Studies about water # should raise this (4 is a reasonable seasonality/solve-time compromise). temporal_resolution: 1 - # Structural (build-time) water-supply configuration. The two switches below - # are independent: tier resolution and the groundwater split can be varied - # separately. + # Structural (build-time) water-supply configuration. Groundwater is always + # part of the aware supply: additive renewable- and non-renewable-groundwater + # bands (annual, per region) on top of the period-bound surface supply, so + # mining emerges endogenously wherever surface plus renewable groundwater + # fall short. Studies wanting a groundwater-free system can constrain it at + # solve time (groundwater_depletion.cap_mm3: 0 caps mining; scarcity pricing + # penalises the renewable draw). supply: - # Convex AWARE scarcity tiers per region-period, so drawing more water costs - # more where water is scarcer. False collapses each region-period pool to one - # flat cap -- a plain availability constraint, cheaper and sufficient where - # water is not the object of study. Scarcity pricing (see water_scarcity) - # requires this. + # Convex AWARE scarcity tiers per region-period (and renewable-groundwater + # bands), so drawing more water costs more where water is scarcer. False + # collapses each pool to one flat cap -- a plain availability constraint, + # cheaper and sufficient where water is not the object of study. Scarcity + # pricing (see water_scarcity) requires this. scarcity_tiers: false - # Additive renewable- and non-renewable-groundwater bands (annual, per - # region) on top of the period-bound surface supply, so mining emerges - # endogenously wherever surface plus renewable groundwater fall short. - groundwater: true # Marginal cost of non-renewable groundwater (USD per m3): a small pumping- # energy cost (~100 m lift at ~0.1 USD/kWh) that also orders groundwater last - # in the merit order. Only active when groundwater is true. + # in the merit order. pumping_cost_usd_per_m3: 0.04 # Non-renewable groundwater ceiling as a multiple of a region's annual # agricultural consumption. Generous and non-binding: mining is drawn only to @@ -335,19 +335,22 @@ water_scarcity: pricing_enabled: false price: 0 # Shadow price, USD per m3 world-equivalent cap_mm3_world_eq: null # Epsilon-constraint cap (Mm3 world-eq); null = no cap - # CF applied to non-renewable groundwater under scarcity pricing: each mined - # m3 is charged nonrenewable_cf * price. AWARE covers renewable water only and - # excludes fossil stocks, so this is a bridging weight that prices a mined m3 - # at the scarcity of the exhausted renewable water it displaces, closing the - # otherwise free substitution into fossil groundwater. 100 is AWARE's - # demand-exceeds-availability cutoff plus a non-renewability premium. Set null - # to leave mining unpriced here, e.g. when groundwater_depletion prices or caps - # it separately (combining both pricings is an error). + # CF applied to non-renewable groundwater under scarcity pricing or capping: + # each mined m3 is charged nonrenewable_cf * price, and counts + # nonrenewable_cf-fold against cap_mm3_world_eq (a joint constraint). AWARE + # covers renewable water only and excludes fossil stocks, so this is a + # bridging weight that prices a mined m3 at the scarcity of the exhausted + # renewable water it displaces, closing the otherwise free substitution into + # fossil groundwater. 100 is AWARE's demand-exceeds-availability cutoff plus + # a non-renewability premium. Set null to leave mining out of both, e.g. + # when groundwater_depletion prices or caps it separately (combining both + # pricings is an error; a null-cf scarcity cap with mining unconstrained + # logs a porosity warning). nonrenewable_cf: 100 # --- section: groundwater_depletion --- # Solve-time pricing and/or capping of accumulated non-renewable groundwater -# depletion (Mm3 mined). Active only when water.supply.groundwater is true; both +# depletion (Mm3 mined). Requires water.data.availability: aware; both # default off. groundwater_depletion: pricing_enabled: false diff --git a/config/schemas/config.schema.yaml b/config/schemas/config.schema.yaml index 6943ad87..56c40a42 100644 --- a/config/schemas/config.schema.yaml +++ b/config/schemas/config.schema.yaml @@ -429,31 +429,25 @@ properties: value on the water side of the model. supply: type: object - required: [scarcity_tiers, groundwater, pumping_cost_usd_per_m3, groundwater_ceiling_factor] + required: [scarcity_tiers, pumping_cost_usd_per_m3, groundwater_ceiling_factor] additionalProperties: false properties: scarcity_tiers: type: boolean description: | - Keep the convex AWARE scarcity tiers per region-period (true), or - collapse each region-period pool to one flat availability cap - (false). Required for water_scarcity pricing or capping, which has - no signal to act on once every surface CF is zero. - groundwater: - type: boolean - description: | - Emit additive annual renewable- and non-renewable-groundwater - bands on top of the period-bound surface supply, so mining emerges - endogenously wherever surface plus renewable groundwater fall - short. Independent of scarcity_tiers. + Keep the convex AWARE scarcity curves (per-region-period surface + tiers and annual renewable-groundwater bands, true), or collapse + each pool to one flat availability cap (false). Required for + water_scarcity pricing or capping, which has no signal to act on + once every CF is zero. pumping_cost_usd_per_m3: type: number minimum: 0 - description: "Marginal cost of non-renewable groundwater (USD per m3); merit-order separator, active only when groundwater is true" + description: "Marginal cost of non-renewable groundwater (USD per m3); merit-order separator" groundwater_ceiling_factor: type: number minimum: 0 - description: "Non-renewable groundwater ceiling as a multiple of annual agricultural consumption; generous and non-binding, active only when groundwater is true" + description: "Non-renewable groundwater ceiling as a multiple of annual agricultural consumption; generous and non-binding" data: type: object required: [availability, surface_reference_start, surface_reference_end, groundwater_trend_start, groundwater_trend_end, huang_reference_year] @@ -530,7 +524,7 @@ properties: nonrenewable_cf: type: [number, "null"] minimum: 0 - description: "CF charged per m3 of non-renewable groundwater under scarcity pricing (AWARE cutoff 100); null leaves mining unpriced here" + description: "CF charged per m3 of non-renewable groundwater under scarcity pricing and capping (AWARE cutoff 100); null leaves mining out of both" groundwater_depletion: type: object diff --git a/docs/configuration.rst b/docs/configuration.rst index 269c8692..7bc937f5 100644 --- a/docs/configuration.rst +++ b/docs/configuration.rst @@ -641,8 +641,7 @@ Water Supply :end-before: # --- section: fertilizer --- * ``water.temporal_resolution`` sets the number of intra-year periods the LP resolves for water (a divisor of 12). The default 1 is annual and cheap; raise it (4 is a reasonable compromise) for any study about water, since at annual resolution the whole year's pool serves every season and the groundwater bands go nearly inert. See :doc:`water`. -* ``water.supply.scarcity_tiers`` keeps the convex AWARE scarcity tiers (true) or collapses each region-period pool to one flat availability cap (false, the default). Required for ``water_scarcity`` pricing or capping. -* ``water.supply.groundwater`` adds annual renewable and non-renewable groundwater bands on top of the period-bound surface supply (default true), so mining emerges endogenously where surface falls short. +* ``water.supply.scarcity_tiers`` keeps the convex AWARE scarcity curves (true) or collapses each pool to one flat availability cap (false, the default). Required for ``water_scarcity`` pricing or capping. Groundwater is always part of the aware supply: annual renewable and non-renewable bands on top of the period-bound surface, so mining emerges endogenously where surface falls short; constrain it at solve time (e.g. ``groundwater_depletion.cap_mm3: 0``) to study a mining-free system. * ``water.data.availability`` selects the water availability dataset: ``aware`` (AWARE 2.0 scarcity curve on WaterGAP volumes, the default) or ``current_use`` (Huang et al. irrigation withdrawals, for validation against present-day use). * ``water.data.huang_reference_year`` selects the year (1971-2010) used for the Huang monthly withdrawals when ``availability`` is ``current_use``. * ``water.irrigation.eta_min`` / ``eta_max`` clip the build-time consumptive-efficiency calibration; ``consumed_fraction`` is the consumption-over-withdrawal ratio used for reporting. diff --git a/docs/data_sources.rst b/docs/data_sources.rst index c9fd2f1a..52d2c111 100644 --- a/docs/data_sources.rst +++ b/docs/data_sources.rst @@ -214,7 +214,7 @@ WaterGAP 2.2e **Provider**: Mueller Schmied et al. (2024), via ISIMIP3a -**Description**: Global hydrological and water-use model output. The model uses the groundwater storage compartment (``groundwstor``; its long-term decline is groundwater depletion), and potential irrigation water consumption in total (``pirruse``) and from groundwater (``pirrusegw``). The surface part (``pirruse - pirrusegw``) is the irrigation surface availability that scales the AWARE scarcity curve; ``pirrusegw`` gives the renewable groundwater band; ``pirruse`` anchors the consumptive-efficiency calibration and the mining ceiling. +**Description**: Global hydrological and water-use model output. The model uses the groundwater storage compartment (``groundwstor``; its long-term decline is groundwater depletion), potential irrigation water consumption in total (``pirruse``) and from groundwater (``pirrusegw``), and all-sector potential groundwater consumption (``ptotusegw``, the denominator of irrigation's share of the depletion trend). The surface part (``pirruse - pirrusegw``) is the irrigation surface availability that scales the AWARE scarcity curve; ``pirrusegw`` (net of irrigation-attributed mining) gives the renewable groundwater bands; ``pirruse`` anchors the consumptive-efficiency calibration and the mining ceiling. **Coverage**: * Spatial: Global, 0.5 degree diff --git a/docs/water.rst b/docs/water.rst index f6480712..891ea288 100644 --- a/docs/water.rst +++ b/docs/water.rst @@ -160,9 +160,11 @@ discharge timing strands that delivery in the wet months and overstates dry-season mining (globally ~265 km3/yr). GLADE therefore keeps AWARE's scarcity structure -- the per-basin CF curve -- -but sets surface volume and timing from WaterGAP's monthly climatological -irrigation surface consumption (:math:`\text{pirruse} - \text{pirrusegw}`, -ISIMIP3a WaterGAP 2.2e). The builder overlays that 0.5-degree WaterGAP field +but sets the curve's draw domain from WaterGAP's joint renewable envelope: +monthly climatological irrigation surface consumption +(:math:`\text{pirruse} - \text{pirrusegw}`, ISIMIP3a WaterGAP 2.2e) plus the +annual renewable-groundwater volume, split at each basin's surface fraction +(see `Source bands`_). The builder overlays the 0.5-degree WaterGAP fields directly with every (model-region, AWARE-basin) intersection. Each regional total is conserved exactly, but its within-region basin split follows WaterGAP's grid-cell delivery rather than AWARE basin area. Where WaterGAP @@ -303,41 +305,46 @@ window (see :doc:`data_sources`). Source bands ------------ -With ``water.supply.groundwater`` two groundwater ``source`` bands expand the -supply envelope so that mining emerges endogenously wherever surface falls short -of demand (it is no longer capped at renewable availability). Unlike surface, -which is period-bound, **groundwater is an annual per-region resource**: an -aquifer integrates recharge over the year and can be pumped in any period. Each -region therefore gets a ``groundwater:{region}`` bus, fed by the two annual bands -and distributed to every period's water bus by free delivery links, so a dry -period can draw the whole year's recharge: +AWARE treats renewable water as **one resource**: its availability is basin +discharge including baseflow, and CF application is source-agnostic. The model +therefore builds each basin's CF curve over the *joint renewable envelope* -- +WaterGAP surface delivery plus renewable groundwater -- and splits it at the +basin's surface fraction. The lower (more abundant) slice is the period-bound +surface delivery; the upper slice is renewable groundwater, the marginal, +costlier-to-access renewable source. Two groundwater ``source`` band families +then expand the supply beyond surface so that mining emerges endogenously +wherever surface falls short of demand. Unlike surface, which is period-bound, +**groundwater is an annual per-region resource**: an aquifer integrates +recharge over the year and can be pumped in any period. Each region therefore +gets a ``groundwater:{region}`` bus, fed by the annual bands and distributed to +every period's water bus by free delivery links, so a dry period can draw the +whole year's recharge: .. csv-table:: - :header: source, Meaning, Sizing (annual, per region), Scarcity / impact + :header: source, Meaning, Sizing, Scarcity / impact - ``renewable`` (surface), "Surface + recharged blue water", "That period's convex surface curve (period-bound)", "AWARE CF -> ``impact:water_scarcity``" - ``groundwater_renewable``, "Recharged groundwater abstraction", ":math:`\max(\text{pirrusegw} - \text{mined},\ 0)`", "AWARE CF + tally on ``impact:groundwater_renewable``" - ``groundwater_nonrenewable``, "Mined (depleting) groundwater", ":math:`\text{ceiling\_factor} \times C` (generous, non-binding)", "Mined volume -> ``impact:groundwater_depletion``" + ``renewable`` (surface), "Surface blue water", "That period's convex surface curve (period-bound)", "AWARE CF -> ``impact:water_scarcity``" + ``groundwater_renewable``, "Recharged groundwater abstraction", ":math:`\max(\text{pirrusegw} - \text{mined}_{irr},\ 0)` (annual), in CF bands of the joint curve's upper slice", "AWARE CF + tally on ``impact:groundwater_renewable``" + ``groundwater_nonrenewable``, "Mined (depleting) groundwater", ":math:`\text{ceiling\_factor} \times C` (annual; generous, non-binding)", "Mined volume -> ``impact:groundwater_depletion``" ``compose_water_supply.py`` writes the surface tiers (per region-period) to ``region_water_tiers.csv`` and the annual groundwater bands (per region) to -``region_groundwater_bands.csv``. The renewable band is the WaterGAP volume, -priced at the region's scarcest surface CF so it is drawn after surface but -before mining. The non-renewable band's capacity is a deliberately generous -ceiling (``water.supply.groundwater_ceiling_factor`` times annual consumption): -the volume actually mined is set endogenously by how far surface plus renewable -groundwater fall short of demand -- the pumping cost keeps the draw minimal, so -the ceiling itself does not bind. The groundwater sizing fields come from -WaterGAP 2.2e via ``build_region_watergap.py`` (see :doc:`data_sources`): mining -is the groundwater-storage decline and renewable groundwater is the recharged -part of irrigation groundwater consumption (:math:`\text{pirrusegw}`). The -subtraction crosses sectors -- the storage decline reflects all users, so heavy -municipal or industrial mining also shrinks the irrigation renewable band (a -conservative attribution). There is -no endogenous inter-period surface storage; current reservoir operation enters +``region_groundwater_bands.csv``. The non-renewable band's capacity is a +deliberately generous ceiling (``water.supply.groundwater_ceiling_factor`` +times annual consumption): the volume actually mined is set endogenously by how +far surface plus renewable groundwater fall short of demand -- the pumping cost +keeps the draw minimal, so the ceiling itself does not bind. The groundwater +sizing fields come from WaterGAP 2.2e via ``build_region_watergap.py`` (see +:doc:`data_sources`): the storage decline reflects all users, so irrigation's +mined volume :math:`\text{mined}_{irr}` is the decline times irrigation's share +of all-sector potential groundwater consumption +(:math:`\text{pirrusegw}/\text{ptotusegw}`), and renewable groundwater is the +recharged remainder of irrigation groundwater consumption. There is no +endogenous inter-period surface storage; current reservoir operation enters through the WaterGAP monthly surface profile, so mining reflects the deficit -under today's regulation. With ``water.supply.groundwater: false`` there are no -groundwater bands and the supply is surface only. +under today's regulation. The ``current_use`` availability source emits no +groundwater bands: its withdrawal pool already contains the +groundwater-supplied part of observed use. Scarcity accounting ------------------- @@ -357,12 +364,13 @@ in ``build_region_water_aware.py`` (as the model draws down a basin's pool its AMD falls and the CF rises), discretised into tiers, and drawn low-CF-first via a negligible merit-order regularizer. At solve time the accumulated scarcity can be priced (``water_scarcity.price``) or capped -(``water_scarcity.cap_mm3_world_eq``). A cap alone is porous when -``water.supply.groundwater`` is on: ``nonrenewable_cf`` applies only under -scarcity *pricing*, so with mining neither priced nor capped the LP can meet -the cap by substituting CF-free mining, deterred only by the pumping cost. -Combine the cap with a ``groundwater_depletion`` price or cap for a closed -sweep (the solve logs a warning otherwise). +(``water_scarcity.cap_mm3_world_eq``). With ``nonrenewable_cf`` set, the cap is +a *joint* constraint charging each mined m3 ``nonrenewable_cf``-fold against +it, mirroring how pricing charges mining -- without that term a bare cap would +be porous (the LP could meet it by substituting CF-free mining, deterred only +by the pumping cost). With ``nonrenewable_cf: null`` the cap binds the scarcity +store alone; combine it with a ``groundwater_depletion`` price or cap for a +closed sweep (the solve logs a porosity warning otherwise). Groundwater depletion accounting -------------------------------- @@ -381,14 +389,15 @@ scarcity pricing alone the CF-free mined band would become the cheapest source wherever the scarcity charge exceeds the pumping cost, and "relief" would be substitution into fossil groundwater rather than conservation. ``water_scarcity.nonrenewable_cf`` therefore charges each mined m3 at -``nonrenewable_cf * water_scarcity.price``. The default 100 -- AWARE's +``nonrenewable_cf * water_scarcity.price`` (and counts it +``nonrenewable_cf``-fold against a scarcity cap). The default 100 -- AWARE's demand-exceeds-availability cutoff plus a non-renewability premium -- is a precautionary anchor pricing a mined m3 at least at the scarcity of the exhausted renewable water it displaces. Set it to ``null`` to study depletion as a separate axis via the ``groundwater_depletion`` options (enabling both pricings together is an error). -The renewable-groundwater band additionally tallies its drawn volume on +The renewable-groundwater bands additionally tally their drawn volume on ``impact:groundwater_renewable`` (via a ``bus3`` output) purely for reporting and as a hook for future policy; it does not affect the baseline solve. @@ -424,12 +433,12 @@ Model components Bus, ``water:source``, ``water_source``, "Free global water source" Bus, ``water:{region}:p{p}``, ``water``, "Regional consumption pool (per period)" Bus, ``water_field:{region}:p{p}``, ``water_field``, "Beneficial/applied water for crops (per period)" - Bus, ``groundwater:{region}``, ``groundwater``, "Annual per-region aquifer pool (when supply.groundwater)" + Bus, ``groundwater:{region}``, ``groundwater``, "Annual per-region aquifer pool (aware availability)" Bus, ``impact:water_scarcity``, ``water_scarcity``, "Accumulated AWARE scarcity" Bus, ``impact:groundwater_depletion``, ``groundwater_depletion``, "Accumulated mined volume" Bus, ``impact:groundwater_renewable``, ``groundwater_renewable``, "Renewable-GW volume tally" Link, ``supply:water:{region}:p{p}:t{n}``, ``water_supply``, "Tiered surface supply (source, CF)" - Link, ``supply:groundwater:{region}:{source}``, ``water_supply``, "Annual groundwater bands (when supply.groundwater)" + Link, ``supply:groundwater:{region}:{source}:b{n}``, ``water_supply``, "Annual groundwater bands (aware availability)" Link, ``deliver:groundwater:{region}:p{p}``, ``groundwater_delivery``, "Annual aquifer -> period pool (free)" Link, ``irrigate:{region}:p{p}``, ``irrigation_delivery``, "Consumption -> field (eta_c)" Store, ``store:impact:water_scarcity``, ``water_scarcity``, "Priced/capped at solve time" diff --git a/docs/workflow.rst b/docs/workflow.rst index e26ca716..e9dc8e7a 100644 --- a/docs/workflow.rst +++ b/docs/workflow.rst @@ -134,7 +134,7 @@ Data Preparation Rules * **Purpose**: Observed per-(region, crop) monthly irrigation demand shares, retimed by iterative proportional fitting so region-month totals follow WaterGAP while each crop's annual total and observed season are preserved **compose_water_supply** - * **Input**: The selected availability source's outputs, plus the WaterGAP groundwater bands when ``water.supply.groundwater`` + * **Input**: The selected availability source's outputs, plus the renewable-groundwater CF bands and consumption anchor for the ``aware`` source * **Output**: ``processing/{name}/water/`` -- ``region_water_tiers.csv`` (per-period surface), ``region_groundwater_bands.csv`` (annual per-region groundwater), and the availability tables copied through * **Script**: ``workflow/scripts/compose_water_supply.py`` * **Purpose**: Group months into the model's intra-year periods and finish the supply tables: keep the convex scarcity curve or collapse it to a flat cap, and emit the groundwater bands diff --git a/tests/config/test.yaml b/tests/config/test.yaml index 5b4f5996..2e64c96b 100644 --- a/tests/config/test.yaml +++ b/tests/config/test.yaml @@ -67,9 +67,8 @@ cropgrids_crops: # Exercise the full-fidelity water path in CI: the shipped default is annual # with collapsed tiers (cheap), so without this nothing would cover the -# seasonal split, the convex scarcity curve or the groundwater bands. +# seasonal split or the convex scarcity curves. water: temporal_resolution: 4 supply: scarcity_tiers: true - groundwater: true diff --git a/tests/test_water_groundwater.py b/tests/test_water_groundwater.py index 5a5abd83..a913af06 100644 --- a/tests/test_water_groundwater.py +++ b/tests/test_water_groundwater.py @@ -132,16 +132,14 @@ def test_monthly_flux_uses_watergap_continental_area(tmp_path): # --------------------------------------------------------------------------- # # Tier decomposition # --------------------------------------------------------------------------- # -def _groundwater_bands(mined: dict, renewable_gw: dict) -> pd.DataFrame: - """Region-indexed band-volume table (the compose groundwater input).""" - regions = sorted(set(mined) | set(renewable_gw)) - return pd.DataFrame( - { - "mined_mm3": [mined.get(r, 0.0) for r in regions], - "renewable_gw_mm3": [renewable_gw.get(r, 0.0) for r in regions], - }, - index=pd.Index(regions, name="region"), - ) +def _renewable_gw_tiers(volumes_cfs: dict) -> pd.DataFrame: + """Per-region renewable-GW CF bands as build_region_water_aware emits them.""" + rows = [ + {"region": region, "tier": tier, "capacity_mm3": cap, "marginal_cf": cf} + for region, bands in volumes_cfs.items() + for tier, (cap, cf) in enumerate(bands) + ] + return pd.DataFrame(rows, columns=["region", "tier", "capacity_mm3", "marginal_cf"]) def _agri(mm3: dict) -> pd.Series: @@ -151,21 +149,33 @@ def _agri(mm3: dict) -> pd.Series: def test_annual_bands_are_per_region_gw_only(): """The bands table is annual per-region groundwater only (surface is separate).""" tiers = _renewable_tiers() # r1 surface = 60, r2 surface = 20 - bands_in = _groundwater_bands({"r1": 25.0, "r2": 100.0}, {"r1": 10.0, "r2": 50.0}) + gw_tiers = _renewable_gw_tiers( + {"r1": [(6.0, 2.0), (4.0, 8.0)], "r2": [(50.0, 30.0)]} + ) agri = _agri({"r1": 40.0, "r2": 12.0}) - out = build_groundwater_bands(tiers, bands_in, agri, ceiling_factor=3.0) + out = build_groundwater_bands( + gw_tiers, tiers, agri, ceiling_factor=3.0, scarcity_tiers=True + ) - # GW-only, annual (no surface, no period/tier columns): one row per source. - assert set(out.columns) == {"region", "source", "capacity_mm3", "marginal_cf"} + # GW-only, annual (no surface, no period columns): band-indexed rows. + assert set(out.columns) == { + "region", + "source", + "band", + "capacity_mm3", + "marginal_cf", + } assert set(out["source"].unique()) == { "groundwater_renewable", "groundwater_nonrenewable", } - # Renewable groundwater at the full WaterGAP volume (annual, no /T, no clip). - renew_gw = out[out.source == "groundwater_renewable"].set_index("region")[ - "capacity_mm3" - ] + # Renewable groundwater keeps its own curve bands (annual, no /T, no clip). + renew_gw = ( + out[out.source == "groundwater_renewable"] + .groupby("region")["capacity_mm3"] + .sum() + ) assert renew_gw["r1"] == pytest.approx(10.0) assert renew_gw["r2"] == pytest.approx(50.0) @@ -177,24 +187,39 @@ def test_annual_bands_are_per_region_gw_only(): assert nonrenew["r2"] == pytest.approx(3.0 * 12.0) -def test_annual_bands_merit_cf_and_fallback(): - """Renewable GW at the scarcest surface CF; non-renewable cf 0; the C=0 - fallback sizes the ceiling from the region's surface scale.""" +def test_annual_bands_curve_cfs_collapse_and_fallback(): + """Renewable GW keeps its curve CFs (collapsing to one cf-0 band with tiers + off); non-renewable cf 0; the C=0 fallback sizes the ceiling from the + region's surface scale.""" tiers = _renewable_tiers() - bands_in = _groundwater_bands({"r1": 25.0, "r2": 0.0}, {"r1": 10.0, "r2": 0.0}) + gw_tiers = _renewable_gw_tiers({"r1": [(6.0, 2.0), (4.0, 8.0)]}) # r1 has a consumption anchor; r2 has none (C absent) -> surface fallback. agri = _agri({"r1": 40.0}) - out = build_groundwater_bands(tiers, bands_in, agri, ceiling_factor=3.0) + out = build_groundwater_bands( + gw_tiers, tiers, agri, ceiling_factor=3.0, scarcity_tiers=True + ) - r1 = out[out.region == "r1"].set_index("source") - # Renewable GW sits at the region's scarcest surface CF (drawn after surface). - assert r1.loc["groundwater_renewable", "marginal_cf"] == pytest.approx(5.0) - assert r1.loc["groundwater_nonrenewable", "marginal_cf"] == pytest.approx(0.0) + r1_renew = out[(out.region == "r1") & (out.source == "groundwater_renewable")] + # The bands carry the AWARE curve slice CFs, not a borrowed surface CF. + assert r1_renew.sort_values("band")["marginal_cf"].tolist() == [2.0, 8.0] + r1_nonrenew = out[(out.region == "r1") & (out.source == "groundwater_nonrenewable")] + assert r1_nonrenew["marginal_cf"].iloc[0] == pytest.approx(0.0) # r2 has no consumption anchor: ceiling falls back to surface scale (20 * 3). r2_nonrenew = out[(out.region == "r2") & (out.source == "groundwater_nonrenewable")] assert r2_nonrenew["capacity_mm3"].iloc[0] == pytest.approx(3.0 * 20.0) + # With scarcity tiers off the renewable bands collapse to one flat cf-0 band. + collapsed = build_groundwater_bands( + gw_tiers, tiers, agri, ceiling_factor=3.0, scarcity_tiers=False + ) + c1 = collapsed[ + (collapsed.region == "r1") & (collapsed.source == "groundwater_renewable") + ] + assert len(c1) == 1 + assert c1["capacity_mm3"].iloc[0] == pytest.approx(10.0) + assert c1["marginal_cf"].iloc[0] == pytest.approx(0.0) + def test_collapse_single_flat_cap(): tiers = _renewable_tiers() diff --git a/tests/test_water_surface.py b/tests/test_water_surface.py index 46396d8b..8a4e21cc 100644 --- a/tests/test_water_surface.py +++ b/tests/test_water_surface.py @@ -2,19 +2,25 @@ # # SPDX-License-Identifier: GPL-3.0-or-later -"""Unit tests for the WaterGAP surface-availability rescaling. +"""Unit tests for the WaterGAP renewable-envelope rescaling and curve split. The AWARE scarcity (CF) curve is kept, but each (region, month) pool volume is -rescaled to WaterGAP's monthly irrigation surface consumption. The regional -total is fixed by the WaterGAP anchor, while a direct grid-to-basin overlay -sets the within-region basin split. These tests pin conservation, that the -direct split overrides the AWARE-area split, and the no-signal fallbacks. +rescaled to WaterGAP's joint renewable envelope: monthly irrigation surface +consumption plus the annual renewable-groundwater volume. The regional totals +are fixed by the WaterGAP anchors, while direct grid-to-basin overlays set the +within-region basin splits. These tests pin conservation, that the direct +split overrides the AWARE-area split, the no-signal fallbacks, and the +volume-conserving surface/groundwater split of the CF curve. """ +import numpy as np import pandas as pd import pytest -from workflow.scripts.build_region_water_aware import scale_pool_to_watergap_surface +from workflow.scripts.build_region_water_aware import ( + build_region_curves, + scale_pool_to_watergap, +) def _cells() -> pd.DataFrame: @@ -35,19 +41,27 @@ def _surface(values: dict) -> pd.Series: return pd.Series(list(values.values()), index=idx) -def _basin_surface(values: dict) -> pd.Series: +def _basin_series(values: dict) -> pd.Series: idx = pd.MultiIndex.from_tuples( values.keys(), names=["region", "basin_id", "month"] ) return pd.Series(list(values.values()), index=idx) +def _gw(values: dict) -> pd.Series: + return pd.Series(values, name="renewable_gw").rename_axis("region") + + +NO_GW = _gw({"r1": 0.0, "r2": 0.0}) +NO_BASIN_GW = _basin_series({}) + + def test_scaled_monthly_totals_match_watergap(): cells = _cells() surface = _surface( {("r1", 1): 4.0, ("r1", 2): 40.0, ("r2", 1): 5.0, ("r2", 2): 0.0} ) - basin_surface = _basin_surface( + basin_surface = _basin_series( { ("r1", 1, 1): 8.0, ("r1", 2, 1): 2.0, @@ -55,15 +69,20 @@ def test_scaled_monthly_totals_match_watergap(): ("r2", 3, 1): 1.0, } ) - scaled, factor = scale_pool_to_watergap_surface(cells, surface, basin_surface) + scaled = scale_pool_to_watergap(cells, surface, NO_GW, basin_surface, NO_BASIN_GW) monthly = scaled.groupby(["region", "month"])["region_pool"].sum() assert monthly[("r1", 1)] == pytest.approx(4.0) # scaled down 10x assert monthly[("r1", 2)] == pytest.approx(40.0) # scaled up 2x (trusted) assert monthly[("r2", 1)] == pytest.approx(5.0) # unchanged assert monthly[("r2", 2)] == pytest.approx(0.0) # WaterGAP delivers nothing - assert factor[("r1", 1, 1)] == pytest.approx(3.2 / 30.0) - assert factor[("r1", 2, 1)] == pytest.approx(0.8 / 10.0) + # The 8:2 WaterGAP overlay split of (r1, month 1). + r1m1 = scaled[(scaled["region"] == "r1") & (scaled["month"] == 1)] + split = r1m1.set_index("basin_id")["region_pool"] + assert split[1] == pytest.approx(3.2) + assert split[2] == pytest.approx(0.8) + # Without groundwater the whole envelope is surface. + assert (scaled.loc[scaled["region_pool"] > 0, "surface_frac"] == 1.0).all() def test_direct_basin_overlay_replaces_aware_area_split_and_preserves_amd0(): @@ -71,7 +90,7 @@ def test_direct_basin_overlay_replaces_aware_area_split_and_preserves_amd0(): surface = _surface( {("r1", 1): 4.0, ("r1", 2): 20.0, ("r2", 1): 5.0, ("r2", 2): 5.0} ) - basin_surface = _basin_surface( + basin_surface = _basin_series( { ("r1", 1, 1): 1.0, ("r1", 2, 1): 9.0, @@ -80,7 +99,7 @@ def test_direct_basin_overlay_replaces_aware_area_split_and_preserves_amd0(): ("r2", 3, 2): 1.0, } ) - scaled, _ = scale_pool_to_watergap_surface(cells, surface, basin_surface) + scaled = scale_pool_to_watergap(cells, surface, NO_GW, basin_surface, NO_BASIN_GW) # amd0 (the CF driver) is untouched. assert list(scaled["amd0"]) == list(cells["amd0"]) @@ -101,11 +120,8 @@ def test_unmapped_surface_delivery_gets_an_explicit_ceiling_cf_tier(): ("r2", 2): 5.0, } ) - basin_surface = _basin_surface({}) - scaled, factor = scale_pool_to_watergap_surface(cells, surface, basin_surface) + scaled = scale_pool_to_watergap(cells, surface, NO_GW, NO_BASIN_GW, NO_BASIN_GW) - assert factor[("r1", 1, 1)] == pytest.approx(0.0) - assert factor[("r1", 2, 1)] == pytest.approx(0.0) m3 = scaled[(scaled["region"] == "r1") & (scaled["month"] == 1)] by_basin = m3.groupby("basin_id")["region_pool"].sum() assert by_basin.sum() == pytest.approx(40.0) @@ -138,28 +154,113 @@ def test_delivery_to_zero_aware_pool_gets_the_aware_ceiling_cf(): ("r2", 2): 5.0, } ) - basin_surface = _basin_surface({("r1", 1, 3): 1.0}) + basin_surface = _basin_series( + { + ("r1", 1, 1): 1.0, + ("r1", 1, 2): 1.0, + ("r1", 1, 3): 1.0, + ("r2", 3, 1): 1.0, + ("r2", 3, 2): 1.0, + } + ) - scaled, _ = scale_pool_to_watergap_surface(cells, surface, basin_surface) + scaled = scale_pool_to_watergap(cells, surface, NO_GW, basin_surface, NO_BASIN_GW) m3 = scaled[(scaled["region"] == "r1") & (scaled["month"] == 3)] assert m3["region_pool"].sum() == pytest.approx(8.0) assert m3["amd0"].iloc[0] == pytest.approx(0.0) -def test_region_without_any_aware_pool_keeps_direct_delivery_at_the_ceiling(): - cells = pd.DataFrame( +def test_groundwater_joins_the_renewable_envelope(): + cells = _cells() + surface = _surface({("r1", 1): 4.0, ("r1", 2): 8.0, ("r2", 1): 5.0, ("r2", 2): 5.0}) + basin_surface = _basin_series( { - "region": ["r1", "r1"], - "basin_id": [1, 1], - "month": [1, 2], - "region_pool": [0.0, 0.0], - "amd0": [0.1, 0.2], + ("r1", 1, 1): 1.0, + ("r1", 2, 1): 1.0, + ("r1", 1, 2): 2.0, + ("r2", 3, 1): 1.0, + ("r2", 3, 2): 1.0, } ) - surface = _surface({("r1", 1): 5.0, ("r1", 2): 3.0}) - basin_surface = _basin_surface({("r1", 1, 1): 5.0, ("r1", 1, 2): 3.0}) - scaled, factor = scale_pool_to_watergap_surface(cells, surface, basin_surface) - assert (factor == 0.0).all() - assert scaled["region_pool"].sum() == pytest.approx(8.0) - assert (scaled["amd0"] == 0.0).all() + # r1's annual renewable GW is 6.0, spread by the pirrusegw overlay 1:2 + # between (basin 1, month 1) and (basin 1, month 2). + gw = _gw({"r1": 6.0, "r2": 0.0}) + basin_gw = _basin_series({("r1", 1, 1): 1.0, ("r1", 1, 2): 2.0}) + + scaled = scale_pool_to_watergap(cells, surface, gw, basin_surface, basin_gw) + + keyed = scaled.set_index(["region", "basin_id", "month"]) + # (r1, basin 1, month 1): surface 2.0 + gw 2.0; (r1, basin 1, month 2): + # surface 8.0 + gw 4.0. Annual gw = 6.0 conserved. + assert keyed.at[("r1", 1, 1), "region_pool"] == pytest.approx(4.0) + assert keyed.at[("r1", 1, 1), "surface_frac"] == pytest.approx(0.5) + assert keyed.at[("r1", 1, 2), "region_pool"] == pytest.approx(12.0) + assert keyed.at[("r1", 1, 2), "surface_frac"] == pytest.approx(8.0 / 12.0) + gw_total = (scaled["region_pool"] * (1.0 - scaled["surface_frac"])).sum() + assert gw_total == pytest.approx(6.0) + # r2 has no groundwater: pure surface. + assert (scaled.loc[scaled["region"] == "r2", "surface_frac"] == 1.0).all() + + +def test_unmapped_groundwater_gets_an_explicit_ceiling_band(): + cells = _cells() + surface = _surface({("r1", 1): 4.0, ("r1", 2): 8.0, ("r2", 1): 5.0, ("r2", 2): 5.0}) + basin_surface = _basin_series( + { + ("r1", 1, 1): 1.0, + ("r1", 1, 2): 1.0, + ("r2", 3, 1): 1.0, + ("r2", 3, 2): 1.0, + } + ) + gw = _gw({"r1": 6.0, "r2": 0.0}) + + scaled = scale_pool_to_watergap(cells, surface, gw, basin_surface, NO_BASIN_GW) + + ceiling = scaled[(scaled["region"] == "r1") & (scaled["basin_id"] == -1)] + assert ceiling["region_pool"].sum() == pytest.approx(6.0) + assert (ceiling["surface_frac"] == 0.0).all() + assert (ceiling["amd0"] == 0.0).all() + + +def test_curve_split_conserves_surface_and_groundwater_volumes(): + long = pd.DataFrame( + { + "region": ["r1", "r1", "r2"], + "month": [1, 2, 1], + "volume": [30.0e6, 20.0e6, 10.0e6], + "amd0": [0.05, 0.05, 0.5], + "surface_frac": [0.6, 1.0, 0.5], + } + ) + surface_tiers, gw_bands = build_region_curves(long, 8, 4) + + surface_by_region_month = surface_tiers.groupby(["region", "month"])[ + "capacity_mm3" + ].sum() + assert surface_by_region_month[("r1", 1)] == pytest.approx(18.0) + assert surface_by_region_month[("r1", 2)] == pytest.approx(20.0) + assert surface_by_region_month[("r2", 1)] == pytest.approx(5.0) + gw_by_region = gw_bands.groupby("region")["capacity_mm3"].sum() + assert gw_by_region["r1"] == pytest.approx(12.0) + assert gw_by_region["r2"] == pytest.approx(5.0) + + +def test_groundwater_takes_the_upper_curve_slice(): + """GW bands sit above the surface tiers of the same basin curve.""" + long = pd.DataFrame( + { + "region": ["r1"], + "month": [1], + "volume": [80.0e6], + "amd0": [0.05], + "surface_frac": [0.5], + } + ) + surface_tiers, gw_bands = build_region_curves(long, 8, 4) + + assert surface_tiers["marginal_cf"].max() <= gw_bands["marginal_cf"].min() + assert (np.diff(gw_bands["marginal_cf"].to_numpy()) >= 0).all() + # An abundant basin's GW slice must not saturate at the CF ceiling. + assert gw_bands["marginal_cf"].iloc[0] < 100.0 diff --git a/tests/test_water_temporal.py b/tests/test_water_temporal.py index 35c341e6..3366a2eb 100644 --- a/tests/test_water_temporal.py +++ b/tests/test_water_temporal.py @@ -69,24 +69,32 @@ def test_groundwater_bands_are_annual_per_region(): # Groundwater is an annual per-region resource (one renewable + one mined band # per region), independent of the number of periods -- not split /T. surface = aggregate_months_to_periods(_monthly_tiers(), 4) - groundwater = pd.DataFrame( - {"mined_mm3": [0.0], "renewable_gw_mm3": [40.0]}, - index=pd.Index(["r"], name="region"), + gw_tiers = pd.DataFrame( + { + "region": ["r", "r"], + "tier": [0, 1], + "capacity_mm3": [25.0, 15.0], + "marginal_cf": [3.0, 9.0], + } ) agri = pd.Series({"r": 100.0}) - bands = build_groundwater_bands(surface, groundwater, agri, ceiling_factor=3.0) + bands = build_groundwater_bands( + gw_tiers, surface, agri, ceiling_factor=3.0, scarcity_tiers=True + ) renewable = bands[bands["source"] == "groundwater_renewable"] nonrenewable = bands[bands["source"] == "groundwater_nonrenewable"] - # One annual band each: renewable = full WaterGAP volume; ceiling = 3 * C. - assert set(bands.columns) == {"region", "source", "capacity_mm3", "marginal_cf"} - assert len(renewable) == 1 - assert renewable["capacity_mm3"].iloc[0] == pytest.approx(40.0) + # Annual bands: renewable = the curve slice volumes; ceiling = 3 * C. + assert set(bands.columns) == { + "region", + "source", + "band", + "capacity_mm3", + "marginal_cf", + } + assert renewable["capacity_mm3"].sum() == pytest.approx(40.0) + assert renewable.sort_values("band")["marginal_cf"].tolist() == [3.0, 9.0] assert len(nonrenewable) == 1 assert nonrenewable["capacity_mm3"].iloc[0] == pytest.approx(300.0) - # Renewable GW borrows the region's scarcest surface CF; mining is cf 0. - assert renewable["marginal_cf"].iloc[0] == pytest.approx( - surface["marginal_cf"].max() - ) assert nonrenewable["marginal_cf"].iloc[0] == 0.0 diff --git a/workflow/rules/analysis.smk b/workflow/rules/analysis.smk index bb669189..8a8cfa10 100644 --- a/workflow/rules/analysis.smk +++ b/workflow/rules/analysis.smk @@ -138,7 +138,7 @@ if config["solving"]["inline_analysis"]: "emissions" ]["ghg_pricing_enabled"], water_scarcity_tiers=config["water"]["supply"]["scarcity_tiers"], - water_groundwater=config["water"]["supply"]["groundwater"], + water_availability=config["water"]["data"]["availability"], water_scarcity_pricing_enabled=lambda w: get_effective_config(w.scenario)[ "water_scarcity" ]["pricing_enabled"], diff --git a/workflow/rules/model.smk b/workflow/rules/model.smk index 8dabd083..d4fb14b6 100644 --- a/workflow/rules/model.smk +++ b/workflow/rules/model.smk @@ -452,7 +452,7 @@ rule solve_model: "ghg_pricing_enabled" ], water_scarcity_tiers=config["water"]["supply"]["scarcity_tiers"], - water_groundwater=config["water"]["supply"]["groundwater"], + water_availability=config["water"]["data"]["availability"], water_scarcity_pricing_enabled=lambda w: get_effective_config(w.scenario)[ "water_scarcity" ]["pricing_enabled"], diff --git a/workflow/rules/retrieve.smk b/workflow/rules/retrieve.smk index 65fedf02..f919a9a1 100644 --- a/workflow/rules/retrieve.smk +++ b/workflow/rules/retrieve.smk @@ -1034,16 +1034,19 @@ rule download_watergap_isimip: public files under files.isimip.org. We fetch the groundwater storage compartment (``groundwstor``; its negative - long-term trend is groundwater depletion, Doll et al. 2014) and the potential + long-term trend is groundwater depletion, Doll et al. 2014), the potential irrigation water consumption, total (``pirruse``) and from groundwater - (``pirrusegw``). The surface part (``pirruse - pirrusegw``) is WaterGAP's + (``pirrusegw``), and all-sector potential groundwater consumption + (``ptotusegw``, the denominator of irrigation's share of the depletion + trend). The surface part (``pirruse - pirrusegw``) is WaterGAP's irrigation surface availability, used to cap the AWARE scarcity curve; - ``pirrusegw`` gives the renewable groundwater band. + ``pirrusegw`` gives the renewable groundwater bands. """ output: groundwstor="data/downloads/watergap/watergap2-2e_gswp3-w5e5_obsclim_histsoc_default_groundwstor_global_monthly_1901_2019.nc", pirruse="data/downloads/watergap/watergap2-2e_gswp3-w5e5_obsclim_histsoc_default_pirruse_global_monthly_1901_2019.nc", pirrusegw="data/downloads/watergap/watergap2-2e_gswp3-w5e5_obsclim_histsoc_default_pirrusegw_global_monthly_1901_2019.nc", + ptotusegw="data/downloads/watergap/watergap2-2e_gswp3-w5e5_obsclim_histsoc_default_ptotusegw_global_monthly_1901_2019.nc", params: base_url="https://files.isimip.org/ISIMIP3a/OutputData/water_global/WaterGAP2-2e/gswp3-w5e5/historical", resources: @@ -1056,7 +1059,7 @@ rule download_watergap_isimip: shell: r""" mkdir -p "$(dirname {output.groundwstor})" - for out in "{output.groundwstor}" "{output.pirruse}" "{output.pirrusegw}"; do + for out in "{output.groundwstor}" "{output.pirruse}" "{output.pirrusegw}" "{output.ptotusegw}"; do curl -L --fail --progress-bar -o "$out" \ "{params.base_url}/$(basename "$out")" >> {log} 2>&1 done diff --git a/workflow/rules/water.smk b/workflow/rules/water.smk index d529b367..f18c93f2 100644 --- a/workflow/rules/water.smk +++ b/workflow/rules/water.smk @@ -84,6 +84,7 @@ rule build_region_water_aware: basins="data/downloads/aware2/AWARE20_Native_CFs_geospatial.gpkg", regions="/{name}/regions.geojson", watergap_surface="/{name}/water/watergap/region_watergap_surface.csv", + watergap_groundwater="/{name}/water/watergap/region_groundwater_depletion.csv", watergap_pirruse=_WATERGAP_ISIMIP.format(var="pirruse"), watergap_pirrusegw=_WATERGAP_ISIMIP.format(var="pirrusegw"), watergap_continentalarea=_WATERGAP_CONTINENTALAREA, @@ -94,6 +95,7 @@ rule build_region_water_aware: monthly_region="/{name}/water/aware/monthly_region_water.csv", region_growing="/{name}/water/aware/region_growing_season_water.csv", tiers="/{name}/water/aware/region_water_tiers.csv", + renewable_gw_tiers="/{name}/water/aware/region_renewable_gw_tiers.csv", group: "prep" resources: @@ -133,13 +135,14 @@ rule build_region_water_current_use: # Rule for regional WaterGAP 2.2e (ISIMIP3a) water fields: irrigation surface -# availability (pirruse - pirrusegw, the cap for the AWARE curve) and the -# groundwater bands (mined = storage trend; renewable = pirrusegw - mined). +# availability (pirruse - pirrusegw, the cap for the AWARE curve) and renewable +# groundwater (pirrusegw minus irrigation's share of the storage-decline trend). rule build_region_watergap: input: groundwstor=_WATERGAP_ISIMIP.format(var="groundwstor"), pirruse=_WATERGAP_ISIMIP.format(var="pirruse"), pirrusegw=_WATERGAP_ISIMIP.format(var="pirrusegw"), + ptotusegw=_WATERGAP_ISIMIP.format(var="ptotusegw"), continentalarea=_WATERGAP_CONTINENTALAREA, regions="/{name}/regions.geojson", params: @@ -225,25 +228,24 @@ def water_availability_inputs(w): def water_groundwater_input(w): - """Groundwater depletion table and consumption anchor (groundwater only).""" - if config["water"]["supply"]["groundwater"]: + """Renewable-GW curve and consumption anchor (aware availability only).""" + if config["water"]["data"]["availability"] == "aware": return { - "groundwater": f"/{w.name}/water/watergap/region_groundwater_depletion.csv", + "renewable_gw_tiers": f"/{w.name}/water/aware/region_renewable_gw_tiers.csv", "region_agri": f"/{w.name}/water/watergap/region_agri_consumption.csv", } return {} # Compose the scenario-agnostic water-supply tables from the selected -# availability source: per-period surface tiers (source "renewable") and, -# with supply.groundwater, the annual per-region groundwater bands. +# availability source: per-period surface tiers (source "renewable") and, for +# the aware source, the annual per-region groundwater bands. rule compose_water_supply: input: unpack(water_availability_inputs), unpack(water_groundwater_input), params: scarcity_tiers=config["water"]["supply"]["scarcity_tiers"], - groundwater=config["water"]["supply"]["groundwater"], availability=config["water"]["data"]["availability"], temporal_resolution=config["water"]["temporal_resolution"], consumed_fraction=config["water"]["irrigation"]["consumed_fraction"], diff --git a/workflow/scripts/build_model/irrigation.py b/workflow/scripts/build_model/irrigation.py index 0748c77b..159172c1 100644 --- a/workflow/scripts/build_model/irrigation.py +++ b/workflow/scripts/build_model/irrigation.py @@ -64,8 +64,8 @@ def calibrate_eta_c( infeasibility check below are both *annual*: at ``temporal_resolution > 1`` surface supply is period-bound, so a region can clear the annual test while an individual period's demand still exceeds that period's surface. Those - periods are met from the annual groundwater bands; with - ``water.supply.groundwater: false`` they fall to slack instead. Values above 1 encode + periods are met from the annual groundwater bands; under the current_use + availability source (no bands) they fall to slack instead. Values above 1 encode deficit irrigation (observed consumption below the GAEZ full requirement); see the module docstring. Regions without baseline irrigation (E = 0) or without observed consumption (C = 0) get 1.0 (no adjustment: the delivery diff --git a/workflow/scripts/build_model/primary_resources.py b/workflow/scripts/build_model/primary_resources.py index e87d3923..69ad5ccd 100644 --- a/workflow/scripts/build_model/primary_resources.py +++ b/workflow/scripts/build_model/primary_resources.py @@ -82,16 +82,17 @@ def add_primary_resources( exceed it, so a seasonal shortfall must draw groundwater). Capacities are already in Mm^3. - **Groundwater** (``groundwater_bands``, groundwater mode only) is instead an - *annual* per-region resource: an aquifer integrates recharge over the year - and can be pumped in any period. Each region gets a ``groundwater:{region}`` - bus fed by two supply links from ``water:source`` -- renewable (WaterGAP - volume, scarcity-priced at the region's scarcest surface CF, tallied on - ``impact:groundwater_renewable``) and non-renewable (a generous ceiling, mined - volume on ``impact:groundwater_depletion``, ordered last by its pumping cost) - -- and free ``groundwater_delivery`` links distribute it to every period bus. - The annual cap therefore lets a dry period draw the whole year's recharge, - unlike surface which is period-bound. + **Groundwater** (``groundwater_bands``, aware availability only) is instead + an *annual* per-region resource: an aquifer integrates recharge over the + year and can be pumped in any period. Each region gets a + ``groundwater:{region}`` bus fed by supply links from ``water:source`` -- + renewable CF bands (the upper slice of the joint AWARE renewable envelope, + scarcity-priced at their own curve CFs and tallied on + ``impact:groundwater_renewable``) and a non-renewable ceiling band (mined + volume on ``impact:groundwater_depletion``, ordered last by its pumping + cost) -- and free ``groundwater_delivery`` links distribute it to every + period bus. The annual cap therefore lets a dry period draw the whole + year's recharge, unlike surface which is period-bound. Because the water itself is free, the unpriced LP is indifferent to which tier it draws from within a region and lands on an arbitrary (high-CF) mix. @@ -181,11 +182,11 @@ def add_primary_resources( ) # Annual per-region groundwater (aquifer). Each region's groundwater:{region} - # bus is fed by renewable and non-renewable supply links (annual caps, same - # impact wiring as the surface tiers) and distributed to every period water - # bus by free delivery links, so the year's recharge is shared across periods - # (a dry period can draw all of it). Empty bands (non-groundwater modes) skip - # this entirely. + # bus is fed by renewable CF bands and a non-renewable ceiling band (annual + # caps, same impact wiring as the surface tiers) and distributed to every + # period water bus by free delivery links, so the year's recharge is shared + # across periods (a dry period can draw all of it). An empty bands table + # (current_use availability) skips this entirely. gw = groundwater_bands[groundwater_bands["region"].isin(water_region_list)] gw = _retain_material_water_capacities(gw, min_water_capacity_mm3) if not gw.empty: @@ -201,7 +202,14 @@ def add_primary_resources( gw_nonrenew = (gw["source"] == "groundwater_nonrenewable").to_numpy() gw_cf = gw["marginal_cf"].to_numpy() n.links.add( - ("supply:groundwater:" + gw["region"] + ":" + gw["source"]).to_numpy(), + ( + "supply:groundwater:" + + gw["region"] + + ":" + + gw["source"] + + ":b" + + gw["band"].astype(int).astype(str) + ).to_numpy(), bus0="water:source", bus1=("groundwater:" + gw["region"]).to_numpy(), bus2=np.where( diff --git a/workflow/scripts/build_region_water_aware.py b/workflow/scripts/build_region_water_aware.py index d716b50f..df179dff 100644 --- a/workflow/scripts/build_region_water_aware.py +++ b/workflow/scripts/build_region_water_aware.py @@ -31,12 +31,18 @@ where ``x`` in [0, 1] is the fraction of the pool drawn. At ``x`` corresponding to AWARE's 2019 irrigation draw this reproduces the published CF. GLADE then -replaces the pool capacity with WaterGAP's surface-delivery envelope while -retaining the AWARE CF curve. The convex CF curve is discretised into -sub-segments (closed-form average CF per segment) and merged across basins into -a **per region-month** merit-order supply curve, binned into ``N_TIERS`` tiers. -The lowest-CF (most abundant) water is drawn first, so a plain LP reproduces the -convex integral with no integrality. +replaces the pool capacity with WaterGAP's **joint renewable envelope** -- +surface delivery plus renewable groundwater, one resource in AWARE's +methodology (availability is basin discharge including baseflow; CF application +is source-agnostic) -- while retaining the AWARE CF curve. The convex CF curve +is discretised into sub-segments (closed-form average CF per segment) and split +at each basin's surface fraction: the lower slice is the period-bound surface +delivery, the upper slice the annual renewable-groundwater buffer (the +marginal, costlier-to-access renewable source). Surface segments merge across +basins into a **per region-month** merit-order supply curve binned into +``N_TIERS`` tiers; groundwater segments merge across basins and months into +**per-region annual** bands. The lowest-CF (most abundant) water is drawn +first, so a plain LP reproduces the convex integral with no integrality. **Monthly resolution.** This script keeps the full monthly signal: it emits one convex tier curve per region *and month* (12 curves per region). The temporal @@ -50,10 +56,13 @@ Outputs (all keyed by model ``region``): -- ``monthly_region_water.csv``: agricultural pool per region-month (m3); +- ``monthly_region_water.csv``: renewable pool per region-month (m3); - ``region_growing_season_water.csv``: annual availability per region; - ``region_water_tiers.csv``: ``region, month, tier, capacity_mm3, marginal_cf`` - -- the per-month convex water-supply curves consumed by ``compose_water_supply``; + -- the per-month convex surface supply curves consumed by + ``compose_water_supply``; +- ``region_renewable_gw_tiers.csv``: ``region, tier, capacity_mm3, marginal_cf`` + -- the annual renewable-groundwater CF bands, on the same curve; All volumes are WaterGAP's: its regional totals are allocated directly to the intersecting AWARE basins before the CF tiers are built. AWARE contributes the @@ -203,42 +212,22 @@ def _subsegment_cf_factors(n_subsegments: int) -> np.ndarray: return np.log((1.0 - a) / (1.0 - b)) / (b - a) -def build_region_month_tiers( - long: pd.DataFrame, n_subsegments: int, n_tiers: int -) -> pd.DataFrame: - """Merit-order convex water-supply tiers per (region, month). +def bin_merit_tiers(seg: pd.DataFrame, keys: list[str], n_tiers: int) -> pd.DataFrame: + """Merge CF segments per key group into equal-volume merit-order tiers. - ``long`` holds one row per (region, month, basin) with columns ``region``, - ``month``, ``volume`` (m3 of agricultural pool in that month) and ``amd0`` - (m3/m2/month). Each row is expanded into ``n_subsegments`` segments of the - convex CF curve, merged per (region, month) in ascending-CF order, and - binned into ``n_tiers`` equal-volume tiers. + ``seg`` holds columns ``keys + [cf, volume]`` (m3). Segments are sorted in + ascending-CF order per group and binned into ``n_tiers`` equal-volume + tiers, each carrying its volume-weighted mean CF. Output columns are + ``keys + [tier, capacity_mm3, marginal_cf]``. """ - factors = _subsegment_cf_factors(n_subsegments) - - long = long[long["volume"] > 0] - if long.empty: - return pd.DataFrame( - columns=["region", "month", "tier", "capacity_mm3", "marginal_cf"] - ) - - amd0 = long["amd0"].to_numpy()[:, None] - with np.errstate(divide="ignore", invalid="ignore"): - cf = np.where(amd0 > 0, AMD_WORLD_AVG / amd0 * factors[None, :], CF_MAX) - cf = np.clip(cf, CF_MIN, CF_MAX) - seg_volume = (long["volume"].to_numpy()[:, None] / n_subsegments) * np.ones_like(cf) - - seg = pd.DataFrame( - { - "region": np.repeat(long["region"].to_numpy(), n_subsegments), - "month": np.repeat(long["month"].to_numpy(), n_subsegments), - "cf": cf.ravel(), - "volume": seg_volume.ravel(), - } - ) + out_columns = [*keys, "tier", "capacity_mm3", "marginal_cf"] + seg = seg[seg["volume"] > 0] + if seg.empty: + return pd.DataFrame(columns=out_columns) tiers = [] - for (region, month), group in seg.groupby(["region", "month"], sort=False): + for key, group in seg.groupby(keys, sort=False): + key = key if isinstance(key, tuple) else (key,) group = group.sort_values("cf") volume = group["volume"].to_numpy() cfs = group["cf"].to_numpy() @@ -256,8 +245,7 @@ def build_region_month_tiers( marginal_cf = float(np.average(cfs[mask], weights=volume[mask])) tiers.append( { - "region": region, - "month": int(month), + **dict(zip(keys, key)), "tier": tier, "capacity_mm3": cap * MM3_PER_M3, "marginal_cf": marginal_cf, @@ -265,30 +253,89 @@ def build_region_month_tiers( ) if not tiers: - return pd.DataFrame( - columns=["region", "month", "tier", "capacity_mm3", "marginal_cf"] - ) + return pd.DataFrame(columns=out_columns) return ( - pd.DataFrame(tiers) - .sort_values(["region", "month", "tier"]) + pd.DataFrame(tiers)[out_columns] + .sort_values(out_columns[:-2]) .reset_index(drop=True) ) -def aggregate_watergap_surface_by_basin( +def build_region_curves( + long: pd.DataFrame, n_subsegments: int, n_tiers: int +) -> tuple[pd.DataFrame, pd.DataFrame]: + """Surface tiers per (region, month) and renewable-GW bands per region. + + ``long`` holds one row per (region, month, basin) with columns ``region``, + ``month``, ``volume`` (m3 of the joint renewable envelope in that month), + ``amd0`` (m3/m2/month) and ``surface_frac`` (the surface share of the + row's volume). Each row is expanded into ``n_subsegments`` segments of the + convex CF curve over its full volume; each segment is then split at the + row's surface fraction -- the lower (more abundant) slice is the + period-bound surface delivery, the upper slice the annual + renewable-groundwater buffer, reflecting groundwater as the basin's + marginal renewable source. Surface segments merge into per (region, month) + tiers; groundwater segments merge across months into per-region annual + bands. Summed capacities reproduce the surface and groundwater envelopes + exactly (the split is volume-conserving). + """ + factors = _subsegment_cf_factors(n_subsegments) + + long = long[long["volume"] > 0] + if long.empty: + empty_surface = pd.DataFrame( + columns=["region", "month", "tier", "capacity_mm3", "marginal_cf"] + ) + empty_gw = pd.DataFrame( + columns=["region", "tier", "capacity_mm3", "marginal_cf"] + ) + return empty_surface, empty_gw + + amd0 = long["amd0"].to_numpy()[:, None] + with np.errstate(divide="ignore", invalid="ignore"): + cf = np.where(amd0 > 0, AMD_WORLD_AVG / amd0 * factors[None, :], CF_MAX) + cf = np.clip(cf, CF_MIN, CF_MAX) + seg_volume = (long["volume"].to_numpy()[:, None] / n_subsegments) * np.ones_like(cf) + + # Split each segment [a, b) of the draw-fraction domain at the row's + # surface fraction f: the part below f is surface, the rest groundwater. + edges = np.linspace(0.0, 1.0, n_subsegments + 1) + a, b = edges[:-1][None, :], edges[1:][None, :] + frac = long["surface_frac"].to_numpy()[:, None] + surface_share = np.clip(np.minimum(b, frac) - a, 0.0, None) / (b - a) + surface_volume = seg_volume * surface_share + gw_volume = seg_volume - surface_volume + + base = { + "region": np.repeat(long["region"].to_numpy(), n_subsegments), + "month": np.repeat(long["month"].to_numpy(), n_subsegments), + "cf": cf.ravel(), + } + surface_seg = pd.DataFrame({**base, "volume": surface_volume.ravel()}) + gw_seg = pd.DataFrame({**base, "volume": gw_volume.ravel()}) + + surface_tiers = bin_merit_tiers(surface_seg, ["region", "month"], n_tiers) + if not surface_tiers.empty: + surface_tiers["month"] = surface_tiers["month"].astype(int) + gw_bands = bin_merit_tiers(gw_seg, ["region"], n_tiers) + return surface_tiers, gw_bands + + +def aggregate_watergap_by_basin( pirruse_path: str, pirrusegw_path: str, continental_area_path: str, basin_cells: gpd.GeoDataFrame, reference_start: int, reference_end: int, -) -> pd.Series: - """Aggregate WaterGAP surface irrigation delivery to region-basin cells. - - Surface delivery is ``max(pirruse - pirrusegw, 0)`` on WaterGAP's native - grid. Aggregating it directly to the AWARE basin intersections preserves - WaterGAP's spatial allocation within each model region before the AWARE CF - tiers are built. +) -> tuple[pd.Series, pd.Series]: + """Aggregate WaterGAP irrigation delivery to region-basin cells. + + Returns two ``(region, basin_id, month)``-indexed series: surface delivery + ``max(pirruse - pirrusegw, 0)`` and groundwater delivery ``pirrusegw``, on + WaterGAP's native grid. Aggregating them directly to the AWARE basin + intersections preserves WaterGAP's spatial allocation within each model + region before the AWARE CF tiers are built. """ irr_total, lat, lon = compute_monthly_flux_raster( pirruse_path, @@ -308,45 +355,83 @@ def aggregate_watergap_surface_by_basin( raise ValueError("WaterGAP irrigation fields do not share a grid") surface = np.clip(irr_total - irr_gw, 0.0, None) + groundwater = np.clip(irr_gw, 0.0, None) res = float(np.abs(np.diff(np.sort(np.unique(lon)))).min()) if lat[0] < lat[-1]: surface = np.flip(surface, axis=1) - - sources = [ - NumPyRasterSource( - surface[month], - xmin=float(lon.min()) - res / 2, - xmax=float(lon.max()) + res / 2, - ymin=float(lat.min()) - res / 2, - ymax=float(lat.max()) + res / 2, - srs_wkt=_WGS84_WKT, - name=f"m{month + 1}", + groundwater = np.flip(groundwater, axis=1) + + def _extract(volumes: np.ndarray, name: str) -> pd.Series: + sources = [ + NumPyRasterSource( + volumes[month], + xmin=float(lon.min()) - res / 2, + xmax=float(lon.max()) + res / 2, + ymin=float(lat.min()) - res / 2, + ymax=float(lat.max()) + res / 2, + srs_wkt=_WGS84_WKT, + name=f"m{month + 1}", + ) + for month in range(12) + ] + aggregated = exact_extract( + sources, + basin_cells, + ["sum"], + include_cols=["region", "basin_id"], + output="pandas", ) - for month in range(12) - ] - aggregated = exact_extract( - sources, - basin_cells, - ["sum"], - include_cols=["region", "basin_id"], - output="pandas", - ) - long = aggregated.melt( - id_vars=["region", "basin_id"], - value_vars=[f"m{month}_sum" for month in range(1, 13)], - var_name="month", - value_name="surface_m3", + long = aggregated.melt( + id_vars=["region", "basin_id"], + value_vars=[f"m{month}_sum" for month in range(1, 13)], + var_name="month", + value_name=name, + ) + long["month"] = long["month"].str.extract(r"m(\d+)_sum").astype(int) + return long.set_index(["region", "basin_id", "month"])[name] + + return _extract(surface, "surface_m3"), _extract(groundwater, "gw_m3") + + +def _basin_allocation( + cells: pd.DataFrame, + regional_anchor: np.ndarray, + basin_volume: pd.Series, + label: str, +) -> np.ndarray: + """Allocate a regional volume anchor over basin cells by direct overlay. + + ``regional_anchor`` is the target volume per cell row (already broadcast to + the row's region/month key); ``basin_volume`` supplies the within-region + shares from WaterGAP's grid-cell overlay. Rows whose region-key carries + anchor volume but no overlay volume get NaN (handled by the caller as an + explicit ceiling-CF tier). + """ + basin_key = ["region", "basin_id", "month"] + cell_index = pd.MultiIndex.from_frame(cells[basin_key]) + region_index = pd.MultiIndex.from_frame(cells[["region", "month"]]) + direct = basin_volume.reindex(cell_index, fill_value=0.0).to_numpy(dtype=float) + direct_total = ( + pd.Series(direct, index=region_index) + .groupby(level=[0, 1]) + .transform("sum") + .to_numpy() ) - long["month"] = long["month"].str.extract(r"m(\d+)_sum").astype(int) - return long.set_index(["region", "basin_id", "month"])["surface_m3"] + target = np.full(len(cells), np.nan) + mapped = direct_total > 0.0 + target[mapped] = regional_anchor[mapped] * direct[mapped] / direct_total[mapped] + target[regional_anchor == 0.0] = 0.0 + return target -def scale_pool_to_watergap_surface( +def scale_pool_to_watergap( cells: pd.DataFrame, surface_m3: pd.Series, + renewable_gw_m3: pd.Series, basin_surface_m3: pd.Series, -) -> tuple[pd.DataFrame, pd.Series]: - """Rescale AWARE pools to WaterGAP surface delivery by region-basin-month. + basin_gw_m3: pd.Series, +) -> pd.DataFrame: + """Rescale AWARE pools to the joint WaterGAP renewable envelope. AWARE ``availability`` is basin river discharge, which counts through-flow discharge as divertible and hugely overstates the surface water accessible @@ -356,67 +441,118 @@ def scale_pool_to_watergap_surface( ``histsoc`` runs operate every GRanD reservoir >= 0.5 km3: the monthly profile of its irrigation surface consumption (``pirruse - pirrusegw``) is regulated, demand-timed delivery. We keep AWARE's scarcity structure -- the - per-basin CF curve (a function of ``amd0``, not volume), but replace the - old area-weighted basin split with WaterGAP's direct grid-cell overlay. The - regional WaterGAP total remains the conservation anchor, while the direct - overlay supplies its within-region basin shares. Delivery without a mapped - AWARE basin is retained on an explicit ceiling-CF tier. - - ``surface_m3`` is indexed by ``(region, month)`` and ``basin_surface_m3`` - by ``(region, basin_id, month)``. The returned factors use the latter index. + per-basin CF curve (a function of ``amd0``, not volume) -- but replace the + volume with WaterGAP's. + + AWARE treats renewable water as ONE resource (its availability is basin + discharge including baseflow; CF application is source-agnostic), so the + curve's draw domain is the *joint* renewable envelope: surface delivery + plus renewable groundwater. Each cell row gets ``region_pool`` = surface + + renewable-GW target and ``surface_frac`` = the surface share of it, along + which the curve is later split into the period-bound surface slice (drawn + first) and the annual renewable-groundwater slice (the marginal, buffered + source, drawn after). Regional WaterGAP totals are the conservation + anchors (surface per region-month; renewable GW annual, spread by the + overlay's basin-month profile); the direct overlay supplies within-region + basin shares. Delivery without a mapped AWARE basin is retained on an + explicit ceiling-CF tier. + + ``surface_m3`` is indexed by ``(region, month)``, ``renewable_gw_m3`` by + ``region`` (annual), and the two basin overlays by + ``(region, basin_id, month)``. """ - region_key = ["region", "month"] - basin_key = ["region", "basin_id", "month"] - cell_index = pd.MultiIndex.from_frame(cells[basin_key]) - region_index = pd.MultiIndex.from_frame(cells[region_key]) + region_index = pd.MultiIndex.from_frame(cells[["region", "month"]]) pool = cells["region_pool"].to_numpy() - direct = basin_surface_m3.reindex(cell_index, fill_value=0.0).to_numpy() - direct_total = ( - pd.Series(direct, index=region_index).groupby(level=[0, 1]).transform("sum") - ) regional_surface = surface_m3.reindex(region_index, fill_value=0.0).to_numpy() + surface_target = _basin_allocation( + cells, regional_surface, basin_surface_m3, "surface" + ) - shares = np.zeros_like(pool) - direct_available = direct_total.to_numpy() > 0.0 - shares[direct_available] = ( - direct[direct_available] / direct_total.to_numpy()[direct_available] + # Annual renewable-GW anchor spread over (basin, month) by the pirrusegw + # overlay profile: gw_target sums to the region's annual renewable volume. + gw_by_region = ( + basin_gw_m3.groupby(level=0).sum().reindex(renewable_gw_m3.index).fillna(0.0) + ) + gw_scale = ( + renewable_gw_m3.div(gw_by_region) + .where(gw_by_region > 0, np.nan) + .where(renewable_gw_m3 > 0, 0.0) ) - target = regional_surface * shares - factor = np.divide(target, pool, out=np.zeros_like(target), where=pool > 0.0) + cell_index = pd.MultiIndex.from_frame(cells[["region", "basin_id", "month"]]) + gw_direct = basin_gw_m3.reindex(cell_index, fill_value=0.0).to_numpy(dtype=float) + gw_target = gw_direct * gw_scale.reindex(cells["region"]).to_numpy(dtype=float) + scaled = cells.copy() - scaled["region_pool"] = target - scaled.loc[(pool <= 0.0) & (target > 0.0), "amd0"] = 0.0 + surface_filled = np.nan_to_num(surface_target, nan=0.0) + gw_filled = np.nan_to_num(gw_target, nan=0.0) + scaled["region_pool"] = surface_filled + gw_filled + with np.errstate(invalid="ignore", divide="ignore"): + scaled["surface_frac"] = np.where( + scaled["region_pool"] > 0.0, + surface_filled / scaled["region_pool"].to_numpy(), + 1.0, + ) + scaled.loc[(pool <= 0.0) & (scaled["region_pool"] > 0.0), "amd0"] = 0.0 # AWARE's zero agricultural pool means that its AMD is non-positive after # re-adding irrigation, so any WaterGAP delivery mapped there carries the # method's maximum CF. If no WaterGAP basin intersection receives delivery # despite a positive regional anchor, retain the volume on an explicit # ceiling-CF tier instead of inventing a lower-scarcity basin allocation. - unmapped = ( + ceiling_rows = [] + unmapped_surface = ( pd.Series( - regional_surface[~direct_available], index=region_index[~direct_available] + np.where(np.isnan(surface_target), regional_surface, 0.0), + index=region_index, ) .groupby(level=[0, 1]) .first() ) - unmapped = unmapped[unmapped > 0.0] - if not unmapped.empty: - ceiling = unmapped.rename("region_pool").reset_index() + unmapped_surface = unmapped_surface[unmapped_surface > 0.0] + if not unmapped_surface.empty: + rows = unmapped_surface.rename("region_pool").reset_index() + rows["surface_frac"] = 1.0 + ceiling_rows.append(rows) + unmapped_gw = renewable_gw_m3[ + (renewable_gw_m3 > 0.0) & ~(gw_by_region > 0.0) + ].rename("region_pool") + if not unmapped_gw.empty: + rows = unmapped_gw.reset_index() + rows["month"] = 1 + rows["surface_frac"] = 0.0 + ceiling_rows.append(rows) + if ceiling_rows: + ceiling = pd.concat(ceiling_rows, ignore_index=True) ceiling["basin_id"] = -1 ceiling["amd0"] = 0.0 scaled = pd.concat( - [scaled, ceiling[["region", "basin_id", "month", "amd0", "region_pool"]]], + [ + scaled, + ceiling[ + [ + "region", + "basin_id", + "month", + "amd0", + "region_pool", + "surface_frac", + ] + ], + ], ignore_index=True, ) logger.warning( - "WaterGAP delivery lacks an AWARE basin intersection in %d " - "region-months: %.0f Mm3 assigned the AWARE ceiling CF", - len(unmapped), - float(unmapped.sum()) * MM3_PER_M3, + "WaterGAP delivery lacks an AWARE basin intersection for %.0f Mm3 " + "surface (%d region-months) and %.0f Mm3 renewable groundwater " + "(%d regions): assigned the AWARE ceiling CF", + float(unmapped_surface.sum()) * MM3_PER_M3, + len(unmapped_surface), + float(unmapped_gw.sum()) * MM3_PER_M3, + len(unmapped_gw), ) - return scaled, pd.Series(factor, index=cell_index) + return scaled if __name__ == "__main__": @@ -455,8 +591,9 @@ def scale_pool_to_watergap_surface( cells["region_pool"] = cells["share"] * cells["basin_pool"] # Replace AWARE's basin-discharge availability volume and timing with - # WaterGAP's monthly irrigation surface consumption. WaterGAP determines - # both the regional surface envelope and its basin allocation, so the CF + # WaterGAP's joint renewable envelope: monthly irrigation surface + # consumption plus the annual renewable-groundwater volume. WaterGAP + # determines the regional envelopes and their basin allocation, so the CF # tiers retain AWARE's scarcity structure without using an area-share proxy # for delivery within a model region. surface_m3 = ( @@ -465,7 +602,13 @@ def scale_pool_to_watergap_surface( )["surface_consumption_mm3"] / MM3_PER_M3 ) - basin_surface_m3 = aggregate_watergap_surface_by_basin( + renewable_gw_m3 = ( + pd.read_csv(snakemake.input.watergap_groundwater).set_index( # type: ignore[name-defined] + "region" + )["renewable_gw_mm3"] + / MM3_PER_M3 + ) + basin_surface_m3, basin_gw_m3 = aggregate_watergap_by_basin( snakemake.input.watergap_pirruse, # type: ignore[name-defined] snakemake.input.watergap_pirrusegw, # type: ignore[name-defined] snakemake.input.watergap_continentalarea, # type: ignore[name-defined] @@ -473,9 +616,11 @@ def scale_pool_to_watergap_surface( int(snakemake.params.surface_start), # type: ignore[name-defined] int(snakemake.params.surface_end), # type: ignore[name-defined] ) - cells, _ = scale_pool_to_watergap_surface(cells, surface_m3, basin_surface_m3) + cells = scale_pool_to_watergap( + cells, surface_m3, renewable_gw_m3, basin_surface_m3, basin_gw_m3 + ) - # Monthly region pool (m3) -- full agricultural availability per region-month. + # Monthly region pool (m3) -- full renewable availability per region-month. monthly_region = ( cells.groupby(["region", "month"], as_index=False)["region_pool"] .sum() @@ -483,12 +628,13 @@ def scale_pool_to_watergap_surface( .sort_values(["region", "month"]) ) - # Per (region, month) convex tiers from the full monthly pool. The seasonal - # bind is applied downstream by the LP's period buses, not here. + # Per (region, month) surface tiers and per-region annual renewable-GW + # bands from the joint monthly curves. The seasonal bind is applied + # downstream by the LP's period buses, not here. tier_input = cells.rename(columns={"region_pool": "volume"})[ - ["region", "month", "volume", "amd0"] + ["region", "month", "volume", "amd0", "surface_frac"] ] - tiers = build_region_month_tiers(tier_input, N_SUBSEGMENTS, N_TIERS) + tiers, gw_bands = build_region_curves(tier_input, N_SUBSEGMENTS, N_TIERS) annual = ( monthly_region.groupby("region")["water_available_m3"] @@ -513,3 +659,7 @@ def scale_pool_to_watergap_surface( tiers_out = Path(snakemake.output.tiers) # type: ignore[name-defined] tiers_out.parent.mkdir(parents=True, exist_ok=True) tiers.to_csv(tiers_out, index=False) + + gw_out = Path(snakemake.output.renewable_gw_tiers) # type: ignore[name-defined] + gw_out.parent.mkdir(parents=True, exist_ok=True) + gw_bands.to_csv(gw_out, index=False) diff --git a/workflow/scripts/build_region_watergap.py b/workflow/scripts/build_region_watergap.py index ef914fd6..bc15c218 100644 --- a/workflow/scripts/build_region_watergap.py +++ b/workflow/scripts/build_region_watergap.py @@ -20,6 +20,8 @@ - ``pirruse`` (kg m-2 s-1 = mm/s): potential irrigation water consumption (the evapotranspired portion), all sources. - ``pirrusegw``: the part of ``pirruse`` supplied from groundwater. +- ``ptotusegw``: potential groundwater consumption of *all* sectors; the + denominator of irrigation's share of groundwater abstraction. The model works on a consumption basis (crops draw beneficial ET, delivered from the consumption-basis pool), so the ``use`` (consumption) variables are the right @@ -38,18 +40,23 @@ discharge timing and strands that delivery in the wet months. The AWARE scarcity (CF) curve is kept; the per region-month volumes are rescaled to this envelope in ``build_region_water_aware.py``. -- **mined groundwater** = the groundwater-storage decline (attributed to - irrigation downstream by the consumption pool). -- **renewable groundwater** = ``max(pirrusegw - mined, 0)``: the recharged part - of irrigation groundwater consumption. +- **mined groundwater** = the groundwater-storage decline. The trend reflects + all users, so irrigation's part is attributed by its share of potential + groundwater consumption (``pirrusegw / ptotusegw``, same basis and window); + a basin mined by municipal or industrial pumping then no longer zeroes + irrigation's renewable band. +- **renewable groundwater** = ``max(pirrusegw - mined_irrigation, 0)``: the + recharged part of irrigation groundwater consumption. Outputs (keyed by model ``region``): - ``region_watergap_surface.csv``: ``region, month, surface_consumption_mm3`` -- monthly climatological irrigation surface consumption, the availability envelope for the AWARE curve; -- ``region_groundwater_depletion.csv``: ``region, mined_mm3, renewable_gw_mm3`` - -- the additive groundwater bands consumed by ``compose_water_supply.py``; +- ``region_groundwater_depletion.csv``: ``region, mined_mm3, + irrigation_gw_share, mined_irrigation_mm3, renewable_gw_mm3`` -- the + renewable-groundwater volume anchor (and mining diagnostics) consumed by + ``build_region_water_aware.py`` and ``compose_water_supply.py``; - ``region_agri_consumption.csv``: ``region, agri_consumption_m3`` -- annual total irrigation consumption (``pirruse``), the demand anchor for ``eta_c`` and the mining ceiling. Replaces the AWARE 2019 ``agri_pHWC`` anchor so that @@ -221,6 +228,7 @@ def aggregate_to_regions( continental_area_path: str = snakemake.input.continentalarea # type: ignore[name-defined] pirruse_path: str = snakemake.input.pirruse # type: ignore[name-defined] pirrusegw_path: str = snakemake.input.pirrusegw # type: ignore[name-defined] + ptotusegw_path: str = snakemake.input.ptotusegw # type: ignore[name-defined] regions_path: str = snakemake.input.regions # type: ignore[name-defined] trend_start: int = int(snakemake.params.trend_start) # type: ignore[name-defined] trend_end: int = int(snakemake.params.trend_end) # type: ignore[name-defined] @@ -252,6 +260,7 @@ def monthly_to_regions(path, variable): irr_total = monthly_to_regions(pirruse_path, "pirruse") irr_gw = monthly_to_regions(pirrusegw_path, "pirrusegw") + total_gw = monthly_to_regions(ptotusegw_path, "ptotusegw") region_index = pd.Index(sorted(regions_gdf["region"]), name="region") @@ -271,13 +280,26 @@ def monthly_to_regions(path, variable): Path(surface_out).parent.mkdir(parents=True, exist_ok=True) surface.to_csv(surface_out, index=False) - # Groundwater bands (annual): mined from the storage trend; renewable = the - # recharged remainder of irrigation groundwater consumption (pirrusegw - mined). + # Groundwater (annual): mined from the storage trend, attributed to + # irrigation by its share of all-sector groundwater consumption; renewable = + # the recharged remainder of irrigation groundwater consumption + # (pirrusegw - mined_irrigation). Regions with a storage decline but no + # potential groundwater use (a climate-driven trend, not abstraction) get + # share 0. + irr_gw_annual = irr_gw.sum(axis=1) + total_gw_annual = total_gw.sum(axis=1) + share = ( + irr_gw_annual.div(total_gw_annual) + .where(total_gw_annual > 0, 0.0) + .clip(0.0, 1.0) + ) depletion = pd.DataFrame({"region": region_index}).assign( mined_mm3=lambda d: d["region"].map(mined * MM3_PER_M3).fillna(0.0), + irrigation_gw_share=lambda d: d["region"].map(share).fillna(0.0), + mined_irrigation_mm3=lambda d: d["mined_mm3"] * d["irrigation_gw_share"], renewable_gw_mm3=lambda d: ( - d["region"].map(irr_gw.sum(axis=1) * MM3_PER_M3).fillna(0.0) - - d["mined_mm3"] + d["region"].map(irr_gw_annual * MM3_PER_M3).fillna(0.0) + - d["mined_irrigation_mm3"] ).clip(lower=0.0), ) Path(depletion_out).parent.mkdir(parents=True, exist_ok=True) diff --git a/workflow/scripts/compose_water_supply.py b/workflow/scripts/compose_water_supply.py index df87e031..593111d2 100644 --- a/workflow/scripts/compose_water_supply.py +++ b/workflow/scripts/compose_water_supply.py @@ -18,33 +18,29 @@ source``): the **per-period surface** supply. Surface is period-bound -- a river cannot be pumped next season without a reservoir -- so each period's cap binds its own draw. -- ``region_groundwater_bands.csv`` (``region, source, capacity_mm3, - marginal_cf``): the **annual per-region groundwater** bands, only in - ``groundwater`` mode. Groundwater is an aquifer, an annual buffer that can be - pumped in any period, so it attaches to a single per-region groundwater bus in - the model build and is shared across periods rather than split per period. +- ``region_groundwater_bands.csv`` (``region, source, band, capacity_mm3, + marginal_cf``): the **annual per-region groundwater** bands, emitted for the + ``aware`` availability source. Groundwater is an aquifer, an annual buffer + that can be pumped in any period, so it attaches to a single per-region + groundwater bus in the model build and is shared across periods rather than + split per period. The renewable bands (``source = groundwater_renewable``) + carry the AWARE CF curve slice computed in ``build_region_water_aware`` (the + upper part of the joint renewable envelope); the non-renewable / mined band + (``source = groundwater_nonrenewable``) is a generous non-binding aquifer + ceiling carrying a real pumping cost that orders it last. Because + groundwater is additive rather than a relabel of surface, mining emerges + endogenously wherever surface plus renewable groundwater fall short -- it is + not capped at renewable availability. The ``current_use`` source emits no + bands: its Huang withdrawal pool already contains the groundwater-supplied + part, so additive bands would double-count it. Keeping the month grouping here means changing the temporal resolution does not re-run the expensive basin overlay. -Two independent switches finish the tables: - -- ``supply.scarcity_tiers``: keep the convex per-period surface scarcity curve, - or collapse each region-period's pool to one flat tier (cf = 0) -- a simple - availability cap for studies where water is not the focus. -- ``supply.groundwater``: additionally emit the annual renewable and - non-renewable groundwater bands. The renewable groundwater (``source = - groundwater_renewable``, WaterGAP volume, priced at the scarcest surface CF) - and the non-renewable / mined band (``source = groundwater_nonrenewable``, a - generous non-binding aquifer ceiling carrying a real pumping cost that orders - it last) meet whatever surface cannot. Because groundwater is additive rather - than a relabel of surface, mining emerges endogenously wherever surface plus - renewable groundwater fall short -- it is no longer capped at renewable - availability. - -The bands are always derived from the *uncollapsed* curve, so the CF that -renewable groundwater borrows stays physical even when the surface table itself -is collapsed to a flat cap. +``supply.scarcity_tiers`` finishes the tables: keep the convex scarcity curves +(per-period surface and annual renewable groundwater), or collapse each pool to +one flat cf = 0 tier/band -- a simple availability cap for studies where water +is not the focus. The monthly and growing-season availability tables are copied through unchanged. """ @@ -56,17 +52,12 @@ import pandas as pd TIER_COLUMNS = ["region", "period", "tier", "capacity_mm3", "marginal_cf", "source"] -GW_BAND_COLUMNS = ["region", "source", "capacity_mm3", "marginal_cf"] +GW_BAND_COLUMNS = ["region", "source", "band", "capacity_mm3", "marginal_cf"] # Number of equal-volume tiers the merged per-period convex curve is binned into # (matches N_TIERS in build_region_water_aware). N_TIERS = 8 -# AWARE characterisation-factor ceiling (matches build_region_water_aware CF_MAX); -# used to price renewable groundwater in regions that have no surface water at all -# (so no surface CF to borrow) -- treat them as maximally scarce. -CF_MAX = 100.0 - def month_to_period(months: np.ndarray, temporal_resolution: int) -> np.ndarray: """Map calendar months (1..12) to period indices (0..T-1) in equal blocks. @@ -133,67 +124,78 @@ def collapse_single(tiers: pd.DataFrame) -> pd.DataFrame: def build_groundwater_bands( + renewable_gw_tiers: pd.DataFrame, surface_tiers: pd.DataFrame, - groundwater: pd.DataFrame, agri_consumption_mm3: pd.Series, ceiling_factor: float, + scarcity_tiers: bool, ) -> pd.DataFrame: """Annual per-region groundwater bands (renewable + non-renewable). - Both bands are *annual*: an aquifer integrates recharge over the year and can + All bands are *annual*: an aquifer integrates recharge over the year and can be pumped in any period, so -- unlike surface, which is period-bound -- they attach to a single per-region groundwater bus in the model build, shared - across all periods. ``surface_tiers`` (the per-period surface curve) supplies - the region's scarcest surface CF and total surface volume; ``groundwater`` is - region-indexed with ``mined_mm3`` and ``renewable_gw_mm3``; - ``agri_consumption_mm3`` is region-indexed annual consumption C (Mm3). - - - ``groundwater_renewable``: the WaterGAP renewable groundwater volume, priced - at the region's scarcest surface CF so it is drawn after surface but before - mining (``CF_MAX`` where the region has no surface water to borrow a CF - from -- such regions are maximally scarce); - - ``groundwater_nonrenewable``: a generous non-binding aquifer ceiling - (``ceiling_factor * C``), cf 0, ordered last by its pumping cost. Falls back - to the region's total surface capacity where consumption C is missing/zero. - The volume actually mined is set endogenously by how far surface plus - renewable groundwater fall short of demand. + across all periods. - Returned per region (columns ``GW_BAND_COLUMNS``); regions cover the full - groundwater table, including those with groundwater but no surface tiers. + - ``groundwater_renewable``: the AWARE CF bands of the renewable-groundwater + slice of the joint renewable envelope (``renewable_gw_tiers``, from + ``build_region_water_aware``). With ``scarcity_tiers`` off they collapse + to one flat cf = 0 band per region, mirroring the surface collapse. + - ``groundwater_nonrenewable``: a generous non-binding aquifer ceiling + (``ceiling_factor * C`` with C the annual consumption anchor, falling + back to the region's total surface capacity where C is zero), cf 0, + ordered last by its pumping cost. The volume actually mined is set + endogenously by how far surface plus renewable groundwater fall short of + demand. """ - regions = groundwater.index - surf = surface_tiers.groupby("region").agg( - max_cf=("marginal_cf", "max"), surface_sum=("capacity_mm3", "sum") + renewable = renewable_gw_tiers.rename(columns={"tier": "band"}).assign( + source="groundwater_renewable" + ) + if not scarcity_tiers and not renewable.empty: + renewable = ( + renewable.groupby("region", as_index=False)["capacity_mm3"] + .sum() + .assign(band=0, marginal_cf=0.0, source="groundwater_renewable") + ) + + regions = pd.Index( + sorted( + set(renewable["region"]) + | set(agri_consumption_mm3.index) + | set(surface_tiers["region"]) + ), + name="region", + ) + surface_sum = ( + surface_tiers.groupby("region")["capacity_mm3"] + .sum() + .reindex(regions) + .fillna(0.0) ) - max_cf = surf["max_cf"].reindex(regions).fillna(CF_MAX) - surface_sum = surf["surface_sum"].reindex(regions).fillna(0.0) - renewable_gw = groundwater["renewable_gw_mm3"].reindex(regions).fillna(0.0) consumption = agri_consumption_mm3.reindex(regions).fillna(0.0) # Ceiling anchor: annual consumption, falling back to surface scale where C = 0. anchor = consumption.where(consumption > 0.0, surface_sum) - ceiling = ceiling_factor * anchor - - base = pd.DataFrame({"region": regions}) - ren = base.assign( - source="groundwater_renewable", - capacity_mm3=renewable_gw.to_numpy(), - marginal_cf=max_cf.to_numpy(), - ) - non = base.assign( - source="groundwater_nonrenewable", - capacity_mm3=ceiling.to_numpy(), - marginal_cf=0.0, + non = pd.DataFrame( + { + "region": regions, + "source": "groundwater_nonrenewable", + "band": 0, + "capacity_mm3": (ceiling_factor * anchor).to_numpy(), + "marginal_cf": 0.0, + } ) - bands = pd.concat([ren, non], ignore_index=True) + + bands = pd.concat([renewable, non], ignore_index=True) bands = bands[bands["capacity_mm3"] > 0.0] return ( - bands[GW_BAND_COLUMNS].sort_values(["region", "source"]).reset_index(drop=True) + bands[GW_BAND_COLUMNS] + .sort_values(["region", "source", "band"]) + .reset_index(drop=True) ) if __name__ == "__main__": scarcity_tiers = bool(snakemake.params.scarcity_tiers) # type: ignore[name-defined] - groundwater_enabled = bool(snakemake.params.groundwater) # type: ignore[name-defined] availability: str = snakemake.params.availability # type: ignore[name-defined] temporal_resolution = int(snakemake.params.temporal_resolution) # type: ignore[name-defined] consumed_fraction = float(snakemake.params.consumed_fraction) # type: ignore[name-defined] @@ -214,14 +216,12 @@ def build_groundwater_bands( else: surface = collapse_single(tiers) - # Groundwater supply: annual per-region bands (region_groundwater_bands.csv). - # Derived from the uncollapsed curve so the CF the renewable band borrows is - # the region's real scarcest surface CF, not the collapsed tier's zero. - if groundwater_enabled: + # Groundwater supply: annual per-region bands (region_groundwater_bands.csv), + # aware source only (the current_use pool already contains the + # groundwater-supplied part of observed use). + if availability == "aware": ceiling_factor = float(snakemake.params.groundwater_ceiling_factor) # type: ignore[name-defined] - groundwater = pd.read_csv(snakemake.input.groundwater).set_index( # type: ignore[name-defined] - "region" - ) + renewable_gw_tiers = pd.read_csv(snakemake.input.renewable_gw_tiers) # type: ignore[name-defined] agri_consumption_mm3 = ( pd.read_csv(snakemake.input.region_agri).set_index("region")[ # type: ignore[name-defined] "agri_consumption_m3" @@ -229,7 +229,11 @@ def build_groundwater_bands( * 1e-6 ) bands = build_groundwater_bands( - tiers, groundwater, agri_consumption_mm3, ceiling_factor + renewable_gw_tiers, + tiers, + agri_consumption_mm3, + ceiling_factor, + scarcity_tiers, ) else: bands = pd.DataFrame(columns=GW_BAND_COLUMNS) diff --git a/workflow/scripts/solve_model/core.py b/workflow/scripts/solve_model/core.py index 5f72b067..f5de5d8b 100644 --- a/workflow/scripts/solve_model/core.py +++ b/workflow/scripts/solve_model/core.py @@ -711,13 +711,35 @@ def add_water_scarcity_cap(n: pypsa.Network, cap_mm3_world_eq: float) -> None: vs water-scarcity Pareto front by sweeping the cap while pricing GHG. Non-renewable groundwater carries no CF and does not count against this - cap; with ``water.supply.groundwater`` on, combine the cap with a - groundwater_depletion price or cap, or the LP can satisfy it by mining - (``run_solve`` warns in that case). + plain cap. With ``water_scarcity.nonrenewable_cf`` set, ``run_solve`` adds + the joint cap (``add_water_scarcity_joint_cap``) instead, which charges + mining against the cap at that CF; with ``nonrenewable_cf: null`` the LP + can satisfy this cap by mining (``run_solve`` warns in that case). """ n.stores.static.at["store:impact:water_scarcity", "e_nom_max"] = cap_mm3_world_eq +def add_water_scarcity_joint_cap( + n: pypsa.Network, cap_mm3_world_eq: float, nonrenewable_cf: float +) -> None: + """Cap scarcity plus CF-weighted mining jointly (epsilon-constraint). + + Adds ``e_scarcity + nonrenewable_cf * e_depletion <= cap`` on the two + accumulating impact stores, mirroring how scarcity *pricing* charges mined + volume at ``nonrenewable_cf``: without the mining term, a scarcity cap is + porous -- the LP can meet it by substituting CF-free mined groundwater, + deterred only by the pumping cost. Must run after + ``n.optimize.create_model()``. + """ + e = n.model.variables["Store-e"].sel(snapshot=n.snapshots[-1]) + lhs = e.sel(Store="store:impact:water_scarcity") + nonrenewable_cf * e.sel( + Store="store:impact:groundwater_depletion" + ) + n.model.add_constraints( + lhs <= cap_mm3_world_eq, name="GlobalConstraint-water_scarcity_joint_cap" + ) + + def add_groundwater_depletion_pricing_to_objective( n: pypsa.Network, price_usd_per_m3: float ) -> None: @@ -725,8 +747,8 @@ def add_groundwater_depletion_pricing_to_objective( Prices the mined volume accumulated on ``store:impact:groundwater_depletion`` (Mm^3) at solve time, mirroring the water-scarcity and GHG pricing. A - positive price shifts irrigation away from groundwater mining. Active only - when ``water.supply.groundwater`` is true (otherwise the store stays empty). + positive price shifts irrigation away from groundwater mining. Requires the + aware availability source (otherwise the store stays empty). Parameters ---------- @@ -1502,15 +1524,18 @@ def run_solve( # Add water-scarcity pricing and/or cap if enabled scarcity_priced = smk.params.water_scarcity_pricing_enabled scarcity_capped = smk.params.water_scarcity_cap is not None + depletion_priced = smk.params.groundwater_pricing_enabled + depletion_capped = smk.params.groundwater_cap is not None + nonrenewable_cf = smk.params.water_scarcity_nonrenewable_cf + groundwater_available = smk.params.water_availability == "aware" if (scarcity_priced or scarcity_capped) and not smk.params.water_scarcity_tiers: raise ValueError( "water_scarcity pricing/capping requires water.supply.scarcity_tiers: " - "with collapsed tiers every surface characterisation factor is zero, " + "with collapsed tiers every characterisation factor is zero, " "so the scarcity store accumulates nothing and the lever is vacuous." ) if scarcity_priced: - nonrenewable_cf = smk.params.water_scarcity_nonrenewable_cf - if nonrenewable_cf is not None and smk.params.groundwater_pricing_enabled: + if nonrenewable_cf is not None and depletion_priced: raise ValueError( "water_scarcity.nonrenewable_cf and groundwater_depletion pricing " "both charge the depletion store; set nonrenewable_cf to null to " @@ -1521,30 +1546,32 @@ def run_solve( float(smk.params.water_scarcity_price), None if nonrenewable_cf is None else float(nonrenewable_cf), ) - if scarcity_capped: + # A cap with nonrenewable_cf set charges mining against the cap via a joint + # constraint, added after model creation below. Without it, the plain + # e_nom_max cap applies -- porous to CF-free mining. + scarcity_joint_cap = ( + scarcity_capped and groundwater_available and nonrenewable_cf is not None + ) + if scarcity_capped and not scarcity_joint_cap: add_water_scarcity_cap(n, float(smk.params.water_scarcity_cap)) + if groundwater_available and not ( + scarcity_priced or depletion_priced or depletion_capped + ): + logger.warning( + "water_scarcity cap is porous: nonrenewable_cf is null and " + "groundwater mining is neither priced nor capped, so the LP " + "can meet the cap by substituting mined groundwater. Set " + "nonrenewable_cf or combine with a groundwater_depletion " + "price or cap for a closed sweep." + ) # Add groundwater-depletion pricing and/or cap if enabled - depletion_priced = smk.params.groundwater_pricing_enabled - depletion_capped = smk.params.groundwater_cap is not None - if (depletion_priced or depletion_capped) and not smk.params.water_groundwater: + if (depletion_priced or depletion_capped) and not groundwater_available: raise ValueError( "groundwater_depletion pricing/capping requires " - "water.supply.groundwater: without the groundwater bands nothing " - "is ever mined, so the lever is vacuous." - ) - if ( - scarcity_capped - and smk.params.water_groundwater - and not (scarcity_priced or depletion_priced or depletion_capped) - ): - # nonrenewable_cf charges mining only under scarcity *pricing*; a bare - # cap leaves CF-free mining as an escape deterred only by pumping cost. - logger.warning( - "water_scarcity cap is porous: groundwater mining carries no CF and " - "is neither priced nor capped, so the LP can meet the cap by " - "substituting mined groundwater. Combine with a " - "groundwater_depletion price or cap for a closed sweep." + "water.data.availability: aware: the current_use pool folds " + "groundwater into observed use, so nothing is ever mined and the " + "lever is vacuous." ) if depletion_priced: add_groundwater_depletion_pricing_to_objective( @@ -1773,6 +1800,11 @@ def run_solve( with _phase("add_residue_feed_constraints"): add_residue_feed_constraints(n, max_feed_fraction, max_feed_fraction_by_country) + if scarcity_joint_cap: + add_water_scarcity_joint_cap( + n, float(smk.params.water_scarcity_cap), float(nonrenewable_cf) + ) + # Deviation penalty constraints (land + feed via production_stability; # diet via diet_stability). Resolve the "calibrated" sentinel once; # downstream objective-breakdown bookkeeping is also gated on `enabled`, diff --git a/workflow/scripts/solve_namespace.py b/workflow/scripts/solve_namespace.py index 119644cf..e6979d93 100644 --- a/workflow/scripts/solve_namespace.py +++ b/workflow/scripts/solve_namespace.py @@ -457,7 +457,7 @@ def rp(path: str) -> str: "biofuel_demand_scale": eff["biomass"]["biofuel_demand_scale"], "ghg_pricing_enabled": eff["emissions"]["ghg_pricing_enabled"], "water_scarcity_tiers": eff["water"]["supply"]["scarcity_tiers"], - "water_groundwater": eff["water"]["supply"]["groundwater"], + "water_availability": eff["water"]["data"]["availability"], "water_scarcity_pricing_enabled": eff["water_scarcity"]["pricing_enabled"], "water_scarcity_price": eff["water_scarcity"]["price"], "water_scarcity_cap": eff["water_scarcity"]["cap_mm3_world_eq"], From 2ee930ed1c649f72916edabaef054e159ccabd22 Mon Sep 17 00:00:00 2001 From: Koen van Greevenbroek Date: Wed, 22 Jul 2026 14:38:22 -0700 Subject: [PATCH 12/12] chore: recalibrate both artefact sets for the merged renewable-water design Full tools/calibrate chains on the final tree (default from config/default.yaml, gbd-anchored from config/gsa.yaml), restamping the provenance for the removed water.supply.groundwater key and the merged renewable envelope. L1 centres are essentially unchanged (default: cropland 1.465, grassland 0.234, feed 0.062, 2 iterations; gbd-anchored converged on the warm start), confirming the water redesign does not disturb the validation-mode calibration solves. --- .../calibration/default/animal_cost.csv | 1970 ++-- .../curated/calibration/default/crop_cost.csv | 7526 +++++++-------- .../default/deviation_penalty.yaml | 10 +- .../calibration/default/exogenous_feed.csv | 2 +- .../calibration/default/food_demand.csv | 2 +- .../calibration/default/grassland_cost.csv | 146 +- .../calibration/default/multi_crop_cost.csv | 828 +- .../calibration/default/provenance.yaml | 5 +- .../calibration/gbd-anchored/animal_cost.csv | 1956 ++-- .../calibration/gbd-anchored/crop_cost.csv | 8318 ++++++++--------- .../gbd-anchored/deviation_penalty.yaml | 4 +- .../gbd-anchored/exogenous_feed.csv | 2 +- .../gbd-anchored/exogenous_forage.csv | 4 +- .../calibration/gbd-anchored/food_demand.csv | 2 +- .../calibration/gbd-anchored/food_waste.yaml | 4 +- .../gbd-anchored/grassland_cost.csv | 136 +- .../gbd-anchored/multi_crop_cost.csv | 826 +- .../calibration/gbd-anchored/provenance.yaml | 5 +- 18 files changed, 10872 insertions(+), 10874 deletions(-) diff --git a/data/curated/calibration/default/animal_cost.csv b/data/curated/calibration/default/animal_cost.csv index 6938d8cc..fc2f076d 100644 --- a/data/curated/calibration/default/animal_cost.csv +++ b/data/curated/calibration/default/animal_cost.csv @@ -1,179 +1,179 @@ product,country,correction_bnusd_per_mt_feed -dairy,AFG,0.14644983410835266 -dairy,AGO,-0.10234086401760578 -dairy,ALB,0.20368649810552597 -dairy,ARE,0.01783207501284778 -dairy,ARG,-0.09486781433224678 -dairy,ARM,0.08944909274578094 +dairy,AFG,0.14581052213907242 +dairy,AGO,-0.10476148314774036 +dairy,ALB,0.02946387231349945 +dairy,ARE,0.015399262309074402 +dairy,ARG,-0.0963076688349247 +dairy,ARM,0.08880676701664925 dairy,ASM,-0.0 -dairy,ATG,-0.10383479297161102 -dairy,AUS,0.008612999692559242 -dairy,AUT,0.15280458331108093 -dairy,AZE,0.06861787289381027 -dairy,BDI,-0.09297323413193226 -dairy,BEL,0.1834448203444481 -dairy,BEN,-0.1523711234331131 -dairy,BFA,-0.16062109917402267 -dairy,BGD,-0.037245487328618765 -dairy,BGR,0.20306109637022018 -dairy,BHS,-0.05763786472380161 -dairy,BIH,0.29675404727458954 -dairy,BLR,0.09814056754112244 -dairy,BLZ,-0.12876390293240547 -dairy,BOL,-0.14311201870441437 -dairy,BRA,-0.09897543489933014 -dairy,BRB,-0.24133126437664032 -dairy,BRN,0.012297526933252811 -dairy,BTN,0.05229637026786804 -dairy,BWA,-0.12357912957668304 -dairy,CAF,-0.10489494912326336 -dairy,CAN,-0.13290974497795105 -dairy,CHE,0.1759401187300682 -dairy,CHL,-0.08723617345094681 -dairy,CHN,0.3026803731918335 -dairy,CIV,-0.15713515877723694 -dairy,CMR,-0.16566967964172363 -dairy,COD,-0.1699833944439888 -dairy,COG,-0.16582904011011124 -dairy,COL,-0.155401311814785 -dairy,COM,-0.12286531552672386 -dairy,CPV,-0.11055051535367966 -dairy,CRI,-0.04385841637849808 -dairy,CUB,-0.13509975373744965 -dairy,CYP,0.19577965885400772 -dairy,CZE,0.1449684053659439 -dairy,DEU,0.16165850311517715 -dairy,DJI,-0.0168414618819952 -dairy,DNK,0.19187518954277039 -dairy,DOM,-0.025776835158467293 -dairy,DZA,0.0572646539658308 -dairy,ECU,-0.1488896682858467 -dairy,EGY,-0.005571229849010706 -dairy,ERI,-0.08567306585609913 -dairy,ESP,0.05021433159708977 -dairy,EST,0.17751184105873108 -dairy,ETH,-0.1193646565079689 -dairy,FIN,0.25297941267490387 -dairy,FJI,0.026251452043652534 -dairy,FRA,0.09333637729287148 -dairy,GAB,-0.027203677222132683 -dairy,GBR,0.20977665483951569 -dairy,GEO,0.06397594790905714 -dairy,GHA,-0.09848720580339432 -dairy,GIN,-0.10227250680327415 -dairy,GMB,-0.12901753559708595 -dairy,GNB,-0.16283077746629715 +dairy,ATG,-0.10716060549020767 +dairy,AUS,0.0077410126104950905 +dairy,AUT,0.152825728058815 +dairy,AZE,0.09436782076954842 +dairy,BDI,-0.09538209438323975 +dairy,BEL,0.18488633632659912 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dairy-buffalo,LUX,-0.0 @@ -280,24 +280,24 @@ dairy-buffalo,MEX,-0.0 dairy-buffalo,MKD,-0.0 dairy-buffalo,MLI,-0.0 dairy-buffalo,MLT,-0.0 -dairy-buffalo,MMR,-0.10308296233415604 +dairy-buffalo,MMR,-0.10703235864639282 dairy-buffalo,MNE,-0.0 dairy-buffalo,MNG,-0.0 dairy-buffalo,MOZ,-0.0 dairy-buffalo,MRT,-0.0 dairy-buffalo,MUS,-0.0 dairy-buffalo,MWI,-0.0 -dairy-buffalo,MYS,-0.008970844035502523 +dairy-buffalo,MYS,-0.011356016853824258 dairy-buffalo,NAM,-0.0 dairy-buffalo,NER,-0.0 dairy-buffalo,NGA,-0.0 dairy-buffalo,NIC,-0.0 dairy-buffalo,NLD,-0.0 dairy-buffalo,NOR,-0.0 -dairy-buffalo,NPL,-0.00844678096473217 +dairy-buffalo,NPL,-0.010690446943044662 dairy-buffalo,NZL,-0.0 dairy-buffalo,OMN,-0.0 -dairy-buffalo,PAK,0.08907283842563629 +dairy-buffalo,PAK,0.08733612298965454 dairy-buffalo,PAN,-0.0 dairy-buffalo,PER,-0.0 dairy-buffalo,PHL,-0.0 @@ -325,7 +325,7 @@ dairy-buffalo,SVK,-0.0 dairy-buffalo,SVN,-0.0 dairy-buffalo,SWE,-0.0 dairy-buffalo,SWZ,-0.0 -dairy-buffalo,SYR,0.01216825656592846 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-meat-sheep,THA,-0.07883534394204617 -meat-sheep,TJK,-0.1035887710750103 -meat-sheep,TKM,-0.14912163466215134 -meat-sheep,TLS,-0.09288297221064568 +meat-sheep,SLE,-0.10271206125617027 +meat-sheep,SLV,-0.050661017186939716 +meat-sheep,SOM,-0.07661781087517738 +meat-sheep,SRB,0.07686769962310791 +meat-sheep,SSD,-0.05801620800048113 +meat-sheep,STP,-0.011357911862432957 +meat-sheep,SUR,-0.0077002267353236675 +meat-sheep,SVK,-0.014622020069509745 +meat-sheep,SVN,0.06892277114093304 +meat-sheep,SWE,0.0741205494850874 +meat-sheep,SWZ,-0.1248386949300766 +meat-sheep,SYR,-0.1302611231803894 +meat-sheep,TCD,-0.08888878859579563 +meat-sheep,TGO,-0.09096866101026535 +meat-sheep,THA,-0.08144369162619114 +meat-sheep,TJK,-0.10785634815692902 +meat-sheep,TKM,-0.15338920801877975 +meat-sheep,TLS,-0.09572357684373856 meat-sheep,TTO,0.0053625027649104595 -meat-sheep,TUN,-0.07205658964812756 -meat-sheep,TUR,-0.07478518597781658 +meat-sheep,TUN,-0.07632417231798172 +meat-sheep,TUR,-0.07905276864767075 meat-sheep,TWN,-0.0 -meat-sheep,TZA,-0.07684984058141708 -meat-sheep,UGA,-0.11858104169368744 -meat-sheep,UKR,-0.001203359104692936 +meat-sheep,TZA,-0.07945819199085236 +meat-sheep,UGA,-0.12284862995147705 +meat-sheep,UKR,-0.0054709394462406635 meat-sheep,URY,-0.003583780489861965 -meat-sheep,USA,-0.0 -meat-sheep,UZB,-0.0985897071659565 -meat-sheep,VEN,-0.06821669079363346 -meat-sheep,VNM,-0.10978647321462631 -meat-sheep,VUT,-0.1004164107143879 -meat-sheep,YEM,-0.07037856243550777 -meat-sheep,ZAF,-0.028218806255608797 -meat-sheep,ZMB,-0.06696736067533493 -meat-sheep,ZWE,-0.366745226085186 +meat-sheep,USA,0.0029612286016345024 +meat-sheep,UZB,-0.10285728797316551 +meat-sheep,VEN,-0.07248426973819733 +meat-sheep,VNM,-0.11405405402183533 +meat-sheep,VUT,-0.10207564011216164 +meat-sheep,YEM,-0.07298691384494305 +meat-sheep,ZAF,-0.07913545146584511 +meat-sheep,ZMB,-0.0695757083594799 +meat-sheep,ZWE,-0.3704327195882797 diff --git a/data/curated/calibration/default/crop_cost.csv b/data/curated/calibration/default/crop_cost.csv index 0490df12..13e35e3a 100644 --- a/data/curated/calibration/default/crop_cost.csv +++ b/data/curated/calibration/default/crop_cost.csv @@ -1,61 +1,61 @@ crop,country,correction_bnusd_per_mha alfalfa,AFG,-0.3613618314266205 alfalfa,AGO,-0.9381504952907562 -alfalfa,ALB,-0.21057342505082488 -alfalfa,ARE,0.8991113305091858 -alfalfa,ARG,-0.4150523543357849 +alfalfa,ALB,-0.29676081612706184 +alfalfa,ARE,0.9148423075675964 +alfalfa,ARG,-0.4239474833011627 alfalfa,ARM,-0.0 alfalfa,ATG,-7.5 -alfalfa,AUS,1.0110264420509338 +alfalfa,AUS,1.2308775782585144 alfalfa,AUT,-0.8089405298233032 -alfalfa,AZE,0.4651461988687515 +alfalfa,AZE,0.7646297961473465 alfalfa,BDI,-0.8992443978786469 alfalfa,BEL,0.045041752979159355 -alfalfa,BEN,0.49790994822978973 +alfalfa,BEN,0.5214256942272186 alfalfa,BFA,-0.0 -alfalfa,BGD,1.3732854723930359 -alfalfa,BGR,0.2313733473420143 +alfalfa,BGD,1.1407039165496826 +alfalfa,BGR,0.19751464575529099 alfalfa,BHS,-4.105992168188095 -alfalfa,BIH,3.9570419043302536 -alfalfa,BLR,-2.5428788363933563 +alfalfa,BIH,3.9614472538232803 +alfalfa,BLR,-2.6194463819265366 alfalfa,BLZ,-0.010105629451572895 alfalfa,BOL,-0.33576732873916626 alfalfa,BRA,-0.653052031993866 -alfalfa,BRB,2.0795974731445312 +alfalfa,BRB,2.134476900100708 alfalfa,BRN,-0.8346583247184753 -alfalfa,BTN,-0.2713441401720047 +alfalfa,BTN,-0.26898743212223053 alfalfa,BWA,-7.5 alfalfa,CAF,-0.9728804230690002 -alfalfa,CAN,-0.5801125466823578 +alfalfa,CAN,-0.5887835621833801 alfalfa,CHE,0.49974900484085083 alfalfa,CHL,-0.8672170341014862 -alfalfa,CHN,0.10282701998949051 +alfalfa,CHN,0.1337210088968277 alfalfa,CIV,7.5 -alfalfa,CMR,0.5399894714355469 +alfalfa,CMR,0.5559331178665161 alfalfa,COD,-0.10757382959127426 alfalfa,COG,-0.09945236518979073 alfalfa,COL,-0.1985142081975937 -alfalfa,COM,0.4136791229248047 +alfalfa,COM,0.43461957573890686 alfalfa,CPV,-7.5 alfalfa,CRI,-0.22407843172550201 alfalfa,CUB,-0.2862354665994644 -alfalfa,CYP,2.6022441387176514 -alfalfa,CZE,-0.053136203438043594 +alfalfa,CYP,3.1579463481903076 +alfalfa,CZE,-0.04436903074383736 alfalfa,DEU,0.06634383089840412 alfalfa,DJI,-0.835690438747406 -alfalfa,DNK,6.519201040267944 +alfalfa,DNK,6.648563385009766 alfalfa,DOM,-1.0948958098888397 -alfalfa,DZA,0.251186840236187 +alfalfa,DZA,0.25876936316490173 alfalfa,ECU,-0.07392867282032967 -alfalfa,EGY,2.0043344497680664 +alfalfa,EGY,2.093764066696167 alfalfa,ERI,-0.8745495080947876 -alfalfa,ESP,-1.1050764322280884 -alfalfa,EST,-0.026798568665981293 -alfalfa,ETH,-0.8142275512218475 +alfalfa,ESP,-1.0627709031105042 +alfalfa,EST,-0.011400247924029827 +alfalfa,ETH,-0.8142275214195251 alfalfa,FIN,-0.0 -alfalfa,FJI,-0.22047322988510132 -alfalfa,FRA,-0.3107704669237137 -alfalfa,GAB,-0.8469574749469757 +alfalfa,FJI,-0.2164454460144043 +alfalfa,FRA,-0.31077052652835846 +alfalfa,GAB,-0.8457782566547394 alfalfa,GBR,0.17221324890851974 alfalfa,GEO,-0.5318324565887451 alfalfa,GHA,-0.9061825275421143 @@ -63,116 +63,116 @@ alfalfa,GIN,-0.8761205673217773 alfalfa,GMB,-7.5 alfalfa,GNB,-0.0 alfalfa,GNQ,-0.7592925727367401 -alfalfa,GRC,-0.715369462966919 -alfalfa,GRD,0.21697093546390533 +alfalfa,GRC,-0.6445825695991516 +alfalfa,GRD,0.23696252703666687 alfalfa,GTM,-0.5668327510356903 alfalfa,GUF,-2.7332428693771362 alfalfa,GUY,-0.07167529128491879 -alfalfa,HND,-0.3269353359937668 -alfalfa,HRV,-3.989750787615776 +alfalfa,HND,-0.3479782044887543 +alfalfa,HRV,-3.7563730627298355 alfalfa,HTI,-0.8184686303138733 -alfalfa,HUN,-0.5287011563777924 +alfalfa,HUN,-2.680657207965851 alfalfa,IDN,-0.14206960052251816 -alfalfa,IND,2.8376086950302124 +alfalfa,IND,2.8952836990356445 alfalfa,IRL,-0.5063982009887695 alfalfa,IRN,-1.2186323404312134 -alfalfa,IRQ,1.8464657068252563 -alfalfa,ISL,-0.7714725732803345 -alfalfa,ISR,1.349403977394104 -alfalfa,ITA,0.28998690843582153 -alfalfa,JAM,1.6476192474365234 -alfalfa,JOR,0.9043538570404053 +alfalfa,IRQ,1.8833762407302856 +alfalfa,ISL,-0.7708936035633087 +alfalfa,ISR,1.3781946897506714 +alfalfa,ITA,0.3310612440109253 +alfalfa,JAM,1.6950284838676453 +alfalfa,JOR,1.1371883153915405 alfalfa,JPN,-0.03383052349090576 -alfalfa,KAZ,-0.5768600106239319 +alfalfa,KAZ,-0.5224663019180298 alfalfa,KEN,-0.4995366930961609 alfalfa,KGZ,-0.8171840310096741 -alfalfa,KHM,-0.847377210855484 -alfalfa,KOR,-0.03990669082850218 -alfalfa,LAO,3.503615453839302 -alfalfa,LBN,2.556659698486328 +alfalfa,KHM,-0.8459306359291077 +alfalfa,KOR,-0.02700718306005001 +alfalfa,LAO,3.50509575009346 +alfalfa,LBN,2.5373342037200928 alfalfa,LBR,-0.9216444790363312 -alfalfa,LBY,-1.7812474370002747 -alfalfa,LKA,-0.7087520360946655 -alfalfa,LSO,-0.9258889853954315 +alfalfa,LBY,-0.8150710463523865 +alfalfa,LKA,-0.7063592076301575 +alfalfa,LSO,-0.38055141270160675 alfalfa,LTU,0.40700384974479675 alfalfa,LUX,-0.31373557448387146 -alfalfa,LVA,-0.554650604724884 -alfalfa,MAR,1.5138700008392334 -alfalfa,MDA,0.2783278673887253 +alfalfa,LVA,-0.5454504787921906 +alfalfa,MAR,0.24066859483718872 +alfalfa,MDA,-0.15995199978351593 alfalfa,MDG,-0.26456888020038605 -alfalfa,MEX,-0.16906669735908508 -alfalfa,MKD,0.17782128602266312 +alfalfa,MEX,-0.17558231949806213 +alfalfa,MKD,0.30116410553455353 alfalfa,MLI,-7.5 alfalfa,MLT,-7.5 -alfalfa,MMR,4.072454929351807 -alfalfa,MNE,1.9367928504943848 -alfalfa,MNG,-0.8200724124908447 +alfalfa,MMR,4.151554107666016 +alfalfa,MNE,1.9767227172851562 +alfalfa,MNG,-0.8199378550052643 alfalfa,MOZ,-0.6108028888702393 -alfalfa,MRT,-0.9963793754577637 -alfalfa,MUS,-0.8317568600177765 +alfalfa,MRT,-0.6804739832878113 +alfalfa,MUS,-0.8304267525672913 alfalfa,MWI,-0.453051894903183 -alfalfa,MYS,-0.5323594212532043 +alfalfa,MYS,-0.5297349691390991 alfalfa,NAM,-7.5 -alfalfa,NER,-0.11278290580958128 +alfalfa,NER,-0.10001542372629046 alfalfa,NGA,-0.9605015814304352 -alfalfa,NIC,-1.8630978465080261 -alfalfa,NLD,7.207625865936279 +alfalfa,NIC,-1.8537434339523315 +alfalfa,NLD,7.094769716262817 alfalfa,NOR,-0.0 -alfalfa,NPL,-0.9011983871459961 +alfalfa,NPL,-0.9006949961185455 alfalfa,NZL,-0.2654487043619156 alfalfa,OMN,-0.0 alfalfa,PAK,-0.8301001787185669 -alfalfa,PAN,-1.0434016585350037 +alfalfa,PAN,-0.09573017060756683 alfalfa,PER,-0.8726335763931274 alfalfa,PHL,-0.07465366646647453 alfalfa,PNG,-0.18700392544269562 alfalfa,POL,-0.4525585323572159 -alfalfa,PRI,-0.8348888158798218 +alfalfa,PRI,-0.1749081201851368 alfalfa,PRT,2.5423593521118164 alfalfa,PRY,-0.2994462549686432 -alfalfa,PSE,0.9433371424674988 -alfalfa,ROU,0.12529699131846428 -alfalfa,RUS,-0.3791075795888901 +alfalfa,PSE,0.9655155539512634 +alfalfa,ROU,0.04112892597913742 +alfalfa,RUS,-0.379828080534935 alfalfa,RWA,-0.22617708891630173 -alfalfa,SAU,0.4857996851205826 -alfalfa,SDN,-0.7491106390953064 +alfalfa,SAU,0.6559031903743744 +alfalfa,SDN,-0.7476268410682678 alfalfa,SEN,-0.0 alfalfa,SLB,-0.6203618347644806 alfalfa,SLE,-0.49844561517238617 -alfalfa,SLV,0.9514340758323669 +alfalfa,SLV,0.9775295853614807 alfalfa,SOM,-0.919923722743988 -alfalfa,SRB,0.17493992671370506 +alfalfa,SRB,0.20243384316563606 alfalfa,SSD,-0.8797294795513153 -alfalfa,STP,0.9617136120796204 +alfalfa,STP,0.992654025554657 alfalfa,SUR,-0.7841159105300903 -alfalfa,SVK,0.022232964634895325 -alfalfa,SVN,0.0366378016769886 +alfalfa,SVK,-0.31761030852794647 +alfalfa,SVN,-0.11728578805923462 alfalfa,SWE,-0.0 alfalfa,SWZ,-0.23123173415660858 alfalfa,SYR,-0.0 alfalfa,TCD,-0.9309031963348389 alfalfa,TGO,-0.03396269865334034 alfalfa,THA,-0.6562699973583221 -alfalfa,TJK,-0.21608976274728775 +alfalfa,TJK,-0.20522144809365273 alfalfa,TKM,-0.0 alfalfa,TLS,-0.038952913135290146 alfalfa,TTO,-0.7199581563472748 -alfalfa,TUN,0.17731326818466187 -alfalfa,TUR,3.2452739477157593 -alfalfa,TWN,1.2630319595336914 +alfalfa,TUN,0.18771500885486603 +alfalfa,TUR,3.3254834413528442 +alfalfa,TWN,1.272720754146576 alfalfa,TZA,-0.6803342700004578 alfalfa,UGA,-0.23317663371562958 -alfalfa,UKR,-0.28949199616909027 +alfalfa,UKR,-0.41509827971458435 alfalfa,URY,-0.6376457810401917 -alfalfa,USA,-0.5051234662532806 -alfalfa,UZB,-1.2331395149230957 -alfalfa,VEN,-0.2790147513151169 -alfalfa,VNM,1.5825157463550568 +alfalfa,USA,-0.553608775138855 +alfalfa,UZB,-1.2214837074279785 +alfalfa,VEN,-0.26397500932216644 +alfalfa,VNM,1.4862912595272064 alfalfa,VUT,-0.10066892206668854 -alfalfa,YEM,-0.843728244304657 -alfalfa,ZAF,-0.608656644821167 +alfalfa,YEM,-0.42849694192409515 +alfalfa,ZAF,-0.3808987885713577 alfalfa,ZMB,-7.5 -alfalfa,ZWE,-0.7590007185935974 +alfalfa,ZWE,-0.7533441483974457 apple,AFG,-0.0 apple,ALB,-0.0 apple,ARG,-7.5 @@ -282,19 +282,19 @@ banana,AFG,-0.0 banana,AGO,-7.5 banana,ALB,-0.0 banana,ARE,-7.5 -banana,ARG,-4.354820013046265 +banana,ARG,-4.390982627868652 banana,ATG,7.5 banana,AUS,-7.5 -banana,BDI,-1.2015145421028137 +banana,BDI,-1.1878576874732971 banana,BEN,-7.5 -banana,BFA,-6.405540704727173 -banana,BGD,-6.855449199676514 -banana,BHS,-7.164050579071045 -banana,BLZ,-6.84993052482605 -banana,BOL,-1.7917412519454956 -banana,BRA,-3.425280809402466 +banana,BFA,-6.381004333496094 +banana,BGD,-6.984182357788086 +banana,BHS,-7.143486738204956 +banana,BLZ,-6.810659646987915 +banana,BOL,-1.8064008951187134 +banana,BRA,-3.574883222579956 banana,BRB,-7.5 -banana,BRN,-2.36167049407959 +banana,BRN,-2.34579336643219 banana,BTN,-7.5 banana,BWA,-0.0 banana,CAF,-7.5 @@ -308,212 +308,212 @@ banana,COM,-7.5 banana,CPV,-7.5 banana,CRI,-7.5 banana,CUB,-7.5 -banana,CYP,-6.05651068687439 +banana,CYP,-5.700850963592529 banana,DJI,-0.0 -banana,DOM,-6.106568098068237 -banana,DZA,-5.15908670425415 -banana,ECU,-6.747934341430664 +banana,DOM,-6.192039251327515 +banana,DZA,-5.142341136932373 +banana,ECU,-6.708709001541138 banana,EGY,-7.5 -banana,ERI,-6.367312908172607 +banana,ERI,-6.293849468231201 banana,ESP,-7.5 -banana,ETH,-0.8949814736843109 -banana,FJI,-7.439409494400024 -banana,FRA,-5.933644533157349 +banana,ETH,-0.8632507920265198 +banana,FJI,-7.386525869369507 +banana,FRA,-5.901601791381836 banana,GAB,-7.5 -banana,GHA,-2.202945113182068 -banana,GIN,0.01654937118291855 +banana,GHA,-2.1945306062698364 +banana,GIN,-2.149530678987503 banana,GMB,-0.0 banana,GNB,-7.5 banana,GNQ,-7.5 banana,GRC,-7.5 -banana,GRD,-1.6923575401306152 -banana,GTM,-4.77807879447937 +banana,GRD,-1.6844731569290161 +banana,GTM,-4.720211863517761 banana,GUF,7.5 banana,GUY,-7.5 banana,HND,-7.5 banana,HTI,-7.5 -banana,IDN,-2.757866859436035 -banana,IND,-1.8975058794021606 +banana,IDN,-2.5174092054367065 +banana,IND,-1.632117509841919 banana,IRN,-7.5 -banana,IRQ,1.670738935470581 +banana,IRQ,1.7421767711639404 banana,ISR,-7.5 -banana,JAM,-3.4384636878967285 +banana,JAM,-3.4207777976989746 banana,JOR,-7.5 -banana,KEN,-2.618582248687744 -banana,KHM,-7.023441314697266 -banana,LAO,-6.180382490158081 +banana,KEN,-2.8209279775619507 +banana,KHM,-6.982274055480957 +banana,LAO,-6.0855393409729 banana,LBN,-7.5 banana,LBR,-7.5 -banana,LKA,-6.167787313461304 +banana,LKA,-6.079521656036377 banana,MAR,-7.5 banana,MDG,-7.5 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-chickpea,SRB,0.6986808478832245 +chickpea,SRB,0.7100631892681122 chickpea,SSD,-0.4944714307785034 chickpea,SUR,0.0 -chickpea,SVK,1.1214795112609863 +chickpea,SVK,0.6583853363990784 chickpea,SVN,4.3599313497543335 chickpea,SWE,-7.5 chickpea,SWZ,-0.7783373892307281 @@ -1385,156 +1385,156 @@ chickpea,SYR,0.09548674523830414 chickpea,TCD,-0.8809231817722321 chickpea,TGO,-0.8288877606391907 chickpea,THA,-0.49598240852355957 -chickpea,TJK,0.002803713083267212 +chickpea,TJK,-0.028737882617861032 chickpea,TKM,-0.33079302310943604 chickpea,TLS,3.7136546187102795 chickpea,TTO,-3.5302809476852417 -chickpea,TUN,0.062041908502578735 +chickpea,TUN,0.05020338296890259 chickpea,TUR,0.6092647910118103 chickpea,TWN,0.24951814115047455 chickpea,TZA,-0.2990361154079437 chickpea,UGA,-0.6356791257858276 -chickpea,UKR,0.5904949903488159 +chickpea,UKR,0.09877524990588427 chickpea,URY,0.22893059253692627 -chickpea,USA,0.749394953250885 +chickpea,USA,0.7760419845581055 chickpea,UZB,-0.2693380117416382 chickpea,VEN,-7.5 chickpea,VNM,3.5667770355939865 -chickpea,YEM,-1.125444233417511 -chickpea,ZAF,-0.8778155446052551 +chickpea,YEM,-1.0762187838554382 +chickpea,ZAF,-0.7949349880218506 chickpea,ZMB,-0.786382257938385 chickpea,ZWE,-1.0339114665985107 -citrus,AFG,-2.178968071937561 -citrus,AGO,-2.72499942779541 -citrus,ALB,-0.5606780629605055 -citrus,ARG,2.630701184272766 -citrus,AUS,-5.08331036567688 -citrus,AZE,-1.5861716270446777 +citrus,AFG,-1.7881840467453003 +citrus,AGO,-2.4044950008392334 +citrus,ALB,0.2522326707839966 +citrus,ARG,3.4693686962127686 +citrus,AUS,-4.9579572677612305 +citrus,AZE,-0.9119182825088501 citrus,BDI,-0.0 -citrus,BEN,-3.3350279331207275 -citrus,BFA,1.6368788480758667 -citrus,BGD,5.558030605316162 +citrus,BEN,-4.160354137420654 +citrus,BFA,2.3583812713623047 +citrus,BGD,5.711952447891235 citrus,BGR,-0.0 -citrus,BHS,2.111612915992737 -citrus,BIH,-6.800734996795654 -citrus,BLZ,2.042192816734314 -citrus,BOL,-4.111902713775635 -citrus,BRA,2.0157177448272705 -citrus,BTN,0.5065940767526627 -citrus,BWA,-0.6231242977082729 -citrus,CAF,-5.015147686004639 -citrus,CHL,-5.704423189163208 +citrus,BHS,2.8838884830474854 +citrus,BIH,-6.645817995071411 +citrus,BLZ,2.2921574115753174 +citrus,BOL,-3.782837152481079 +citrus,BRA,2.6709539890289307 +citrus,BTN,-0.08558005094528198 +citrus,BWA,-0.10126718878746033 +citrus,CAF,-4.853981018066406 +citrus,CHL,-5.089515447616577 citrus,CHN,-7.5 -citrus,CIV,-0.6990315914154053 -citrus,CMR,-4.619676113128662 -citrus,COD,-1.0098663568496704 -citrus,COG,-4.916575193405151 -citrus,COL,0.9801430702209473 +citrus,CIV,-0.5897452235221863 +citrus,CMR,-4.426577091217041 +citrus,COD,-0.5138782858848572 +citrus,COG,-4.747755289077759 +citrus,COL,1.5979211926460266 citrus,COM,-0.0 citrus,CPV,-0.0 citrus,CRI,7.5 -citrus,CUB,-2.531997561454773 -citrus,CYP,-1.2415257692337036 +citrus,CUB,-2.1568212509155273 +citrus,CYP,-0.7001399397850037 citrus,DOM,7.5 -citrus,DZA,-7.403381586074829 -citrus,ECU,2.1343547701835632 -citrus,EGY,-0.0 +citrus,DZA,-6.729033946990967 +citrus,ECU,2.509563624858856 +citrus,EGY,-5.83764123916626 citrus,ERI,-0.0 -citrus,ESP,-1.5414900183677673 -citrus,ETH,1.119479775428772 +citrus,ESP,-1.1703241765499115 +citrus,ETH,1.3903900980949402 citrus,FJI,-7.5 -citrus,FRA,-0.6967107504606247 -citrus,GAB,-6.096924543380737 -citrus,GEO,-1.1136439144611359 -citrus,GHA,-1.690492868423462 -citrus,GIN,1.2948395609855652 -citrus,GRC,4.286113262176514 -citrus,GTM,-0.27176421135663986 +citrus,FRA,-0.1778411716222763 +citrus,GAB,-6.025281190872192 +citrus,GEO,-0.9436023831367493 +citrus,GHA,-1.489461898803711 +citrus,GIN,-0.20455220341682434 +citrus,GRC,5.408078670501709 +citrus,GTM,0.3026319891214371 citrus,GUY,-7.5 citrus,HND,7.5 -citrus,HRV,-4.9848997592926025 -citrus,HTI,-2.283531904220581 +citrus,HRV,-3.0279471278190613 +citrus,HTI,-1.8866263926029205 citrus,IDN,-7.5 -citrus,IND,-0.8638404011726379 -citrus,IRN,-5.104993104934692 +citrus,IND,-0.35908883810043335 +citrus,IRN,-4.53027081489563 citrus,IRQ,-0.0 citrus,ISR,-7.5 -citrus,ITA,-3.4349728375673294 +citrus,ITA,-2.6624313294887543 citrus,JAM,-7.5 -citrus,JOR,-0.5974596589803696 +citrus,JOR,0.2916247621178627 citrus,JPN,-7.5 -citrus,KEN,5.66919732093811 -citrus,KHM,-2.937056541442871 -citrus,KOR,1.6982207000255585 -citrus,LAO,-2.437874972820282 -citrus,LBN,-7.5 -citrus,LBR,-4.375595688819885 -citrus,LBY,1.008392572402954 -citrus,LKA,3.635963559150696 +citrus,KEN,6.4802470207214355 +citrus,KHM,-2.6169046759605408 +citrus,KOR,2.386268377304077 +citrus,LAO,-2.0763182640075684 +citrus,LBN,-7.459336757659912 +citrus,LBR,-4.158993124961853 +citrus,LBY,1.669843316078186 +citrus,LKA,3.9911962747573853 citrus,LSO,-0.0 -citrus,MAR,1.2535122632980347 -citrus,MDG,-4.982327461242676 -citrus,MEX,2.9785420894622803 +citrus,MAR,1.5788108110427856 +citrus,MDG,-4.818448066711426 +citrus,MEX,3.42676043510437 citrus,MKD,-0.0 -citrus,MLI,2.1791536808013916 -citrus,MLT,-5.774165868759155 +citrus,MLI,3.09515380859375 +citrus,MLT,-5.226714849472046 citrus,MMR,-0.0 -citrus,MNE,1.0202890634536743 -citrus,MOZ,-2.990760564804077 +citrus,MNE,1.6853911876678467 +citrus,MOZ,-2.660841464996338 citrus,MRT,-0.0 -citrus,MUS,-2.924850344657898 -citrus,MWI,-0.052566349506378174 -citrus,MYS,0.6273860335350037 -citrus,NAM,-2.361790895462036 +citrus,MUS,-2.593141198158264 +citrus,MWI,0.550665408372879 +citrus,MYS,1.1762738823890686 +citrus,NAM,-1.9853073358535767 citrus,NER,-0.0 -citrus,NGA,-4.425179481506348 -citrus,NIC,3.5659272521734238 -citrus,NPL,1.2544666975736618 -citrus,NZL,3.240681290626526 +citrus,NGA,-4.213005781173706 +citrus,NIC,3.845576085150242 +citrus,NPL,1.757670521736145 +citrus,NZL,4.058375239372253 citrus,OMN,7.5 -citrus,PAK,-0.6828719899058342 -citrus,PAN,4.259383201599121 -citrus,PER,6.315829277038574 -citrus,PHL,3.4516422748565674 +citrus,PAK,-0.17374292016029358 +citrus,PAN,4.684230327606201 +citrus,PER,7.155638217926025 +citrus,PHL,3.833287477493286 citrus,PNG,-0.0 -citrus,PRI,-7.19201922416687 -citrus,PRT,-7.1773974895477295 -citrus,PRY,-3.112849712371826 -citrus,PSE,-3.1344105005264282 -citrus,RUS,-2.2632294297218323 -citrus,RWA,0.300917892716825 -citrus,SAU,-1.7039645910263062 -citrus,SDN,0.6721886396408081 -citrus,SEN,0.4120938777923584 -citrus,SLE,-1.8733981847763062 -citrus,SLV,3.443253368139267 -citrus,SOM,-2.7673754692077637 -citrus,SSD,-4.639884948730469 +citrus,PRI,-6.869824171066284 +citrus,PRT,-6.901605129241943 +citrus,PRY,-2.7603299617767334 +citrus,PSE,-2.327911138534546 +citrus,RUS,-1.7552374303340912 +citrus,RWA,0.4972211495041847 +citrus,SAU,-1.2589111328125 +citrus,SDN,1.278346300125122 +citrus,SEN,0.9289950877428055 +citrus,SLE,-1.7459343075752258 +citrus,SLV,4.3945876359939575 +citrus,SOM,-2.4212050437927246 +citrus,SSD,-4.452408790588379 citrus,SUR,-7.5 citrus,SVN,-0.0 -citrus,SWZ,-4.70198392868042 -citrus,SYR,0.8654991686344147 -citrus,TGO,1.9096110463142395 -citrus,THA,-7.280418157577515 +citrus,SWZ,-4.5141754150390625 +citrus,SYR,1.5051024556159973 +citrus,TGO,2.1985016465187073 +citrus,THA,-6.93937611579895 citrus,TJK,-7.5 citrus,TKM,-0.0 -citrus,TLS,0.6549955377122387 -citrus,TTO,-4.28690505027771 -citrus,TUN,-0.33401147462427616 -citrus,TUR,1.9390830993652344 +citrus,TLS,1.2000060379505157 +citrus,TTO,-4.065426588058472 +citrus,TUN,0.2673484180122614 +citrus,TUR,2.6066606044769287 citrus,TWN,-0.0 -citrus,TZA,-5.118908405303955 +citrus,TZA,-4.961418867111206 citrus,UGA,-0.0 -citrus,URY,-1.708142340183258 -citrus,USA,-1.7638187408447266 -citrus,UZB,-6.856789588928223 -citrus,VEN,-0.47123342752456665 -citrus,VNM,-5.318417072296143 +citrus,URY,-1.1278719305992126 +citrus,USA,-0.7761852741241455 +citrus,UZB,-6.501989841461182 +citrus,VEN,0.07321996241807938 +citrus,VNM,-4.902916789054871 citrus,VUT,-0.0 -citrus,YEM,-4.647449493408203 -citrus,ZAF,-3.5522665977478027 -citrus,ZMB,-2.590416669845581 -citrus,ZWE,-0.9626896679401398 +citrus,YEM,-3.6213693618774414 +citrus,ZAF,-2.735687494277954 +citrus,ZMB,-2.4724771976470947 +citrus,ZWE,-1.6319567263126373 cocoa,AGO,-0.7186420559883118 cocoa,BDI,0.17092011123895645 cocoa,BEN,0.03707966208457947 @@ -1562,7 +1562,7 @@ cocoa,GRD,-0.01948007568717003 cocoa,GTM,4.467069387435913 cocoa,GUF,-0.0 cocoa,GUY,-0.02507608849555254 -cocoa,HND,-0.5473648756742477 +cocoa,HND,-0.6260586977005005 cocoa,HTI,-0.3025696203112602 cocoa,IDN,0.24130182713270187 cocoa,IND,-0.18498730659484863 @@ -1577,9 +1577,9 @@ cocoa,MEX,-0.040098097175359726 cocoa,MWI,1.8081773519515991 cocoa,MYS,0.06002945080399513 cocoa,NGA,-0.49385377764701843 -cocoa,NIC,0.5765094757080078 +cocoa,NIC,0.581375926733017 cocoa,PAN,-0.6694892048835754 -cocoa,PER,-0.45842844247817993 +cocoa,PER,-0.539372593164444 cocoa,PHL,-0.006441449630074203 cocoa,PNG,-0.09799185022711754 cocoa,PRY,-0.0 @@ -1599,82 +1599,82 @@ cocoa,UGA,-0.09800025075674057 cocoa,VEN,-0.12239008396863937 cocoa,VNM,0.08234326168894768 cocoa,VUT,0.4530647546052933 -coconut,AGO,-1.5774341821670532 -coconut,BDI,-2.1970887184143066 -coconut,BEN,-1.4505464434623718 -coconut,BGD,-2.761626362800598 +coconut,AGO,-1.5762559175491333 +coconut,BDI,-2.192922592163086 +coconut,BEN,-1.4503357410430908 +coconut,BGD,-3.4808712005615234 coconut,BHS,-7.5 -coconut,BLZ,-1.6392014026641846 -coconut,BOL,-2.574064612388611 -coconut,BRA,-3.213463068008423 +coconut,BLZ,-1.6382597088813782 +coconut,BOL,-2.5684304237365723 +coconut,BRA,-3.2073776721954346 coconut,BRB,-7.5 -coconut,BRN,-1.5216951370239258 +coconut,BRN,-1.520853877067566 coconut,CAF,7.5 coconut,CHN,-7.5 -coconut,CIV,-1.543723225593567 -coconut,CMR,-1.5802450776100159 -coconut,COD,-1.532392978668213 -coconut,COG,-1.5728198289871216 -coconut,COL,-6.126348972320557 -coconut,COM,-1.5600695610046387 -coconut,CPV,-1.7142897248268127 -coconut,CRI,-2.484104037284851 -coconut,CUB,-1.7780026197433472 -coconut,DOM,-4.3737499713897705 -coconut,ECU,-2.0065414905548096 -coconut,FJI,-4.393575191497803 -coconut,GAB,-1.5805574655532837 -coconut,GHA,-1.7268136143684387 -coconut,GIN,-1.588542103767395 -coconut,GNB,-1.606259822845459 -coconut,GNQ,-1.5299826264381409 +coconut,CIV,-1.543009102344513 +coconut,CMR,-1.5793508887290955 +coconut,COD,-1.5315438508987427 +coconut,COG,-1.5715954899787903 +coconut,COL,-6.123933792114258 +coconut,COM,-1.5593237280845642 +coconut,CPV,-5.190864086151123 +coconut,CRI,-2.4795128107070923 +coconut,CUB,-1.7764917612075806 +coconut,DOM,-4.371025562286377 +coconut,ECU,-2.0038623213768005 +coconut,FJI,-4.391778469085693 +coconut,GAB,-1.5791979432106018 +coconut,GHA,-1.7251099944114685 +coconut,GIN,-1.587359368801117 +coconut,GNB,-1.604884386062622 +coconut,GNQ,-1.5294143557548523 coconut,GRD,-7.5 -coconut,GTM,-1.724228858947754 -coconut,GUF,2.4241586923599243 -coconut,GUY,-1.1917908191680908 -coconut,HND,-1.9813215136528015 -coconut,HTI,-1.6073880195617676 -coconut,IDN,-1.4917396903038025 -coconut,IND,-1.8656069040298462 -coconut,JAM,-1.9041555523872375 -coconut,KEN,-0.6794317960739136 -coconut,KHM,-1.8840535879135132 -coconut,LAO,-2.0527263879776 -coconut,LBR,-1.5943560004234314 -coconut,LKA,-1.7387292385101318 -coconut,MDG,-1.4880717992782593 -coconut,MEX,-1.662992238998413 -coconut,MMR,-2.1530044078826904 -coconut,MOZ,-1.217850685119629 -coconut,MUS,-1.3864623308181763 -coconut,MWI,-2.3598703145980835 -coconut,MYS,-2.2858792543411255 -coconut,NGA,-1.8388160467147827 -coconut,NIC,-1.664277195930481 +coconut,GTM,-1.7225050926208496 +coconut,GUF,2.4271916151046753 +coconut,GUY,-1.1912091374397278 +coconut,HND,-1.9788129329681396 +coconut,HTI,-1.6064021587371826 +coconut,IDN,-1.4896296262741089 +coconut,IND,-1.8636784553527832 +coconut,JAM,-1.9018797278404236 +coconut,KEN,-0.706580400466919 +coconut,KHM,-1.882003903388977 +coconut,LAO,-2.04996120929718 +coconut,LBR,-1.5934049487113953 +coconut,LKA,-1.7372556924819946 +coconut,MDG,-1.4876413345336914 +coconut,MEX,-1.6606794595718384 +coconut,MMR,-2.149049758911133 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foxtail-millet,GNB,-0.4469609558582306 foxtail-millet,GRC,-0.0 foxtail-millet,HUN,7.5 -foxtail-millet,IND,-0.4363897442817688 +foxtail-millet,IND,-0.43558384478092194 foxtail-millet,IRN,-1.0423398613929749 foxtail-millet,IRQ,-0.8328883051872253 -foxtail-millet,KAZ,-0.29122042655944824 +foxtail-millet,KAZ,-0.2774594873189926 foxtail-millet,KEN,-0.49875615537166595 foxtail-millet,KGZ,-0.7159605026245117 foxtail-millet,KHM,-0.0 foxtail-millet,KOR,-0.40312229096889496 foxtail-millet,LAO,0.0 -foxtail-millet,LBY,-0.5144516825675964 +foxtail-millet,LBY,-0.5011973977088928 foxtail-millet,LKA,-1.034119188785553 foxtail-millet,LSO,7.5 -foxtail-millet,MAR,-0.7614437341690063 +foxtail-millet,MAR,-0.7645474970340729 foxtail-millet,MDA,3.4062991440296173 foxtail-millet,MLI,-0.3181726336479187 foxtail-millet,MMR,-0.46037325263023376 @@ -2427,7 +2427,7 @@ foxtail-millet,THA,-0.7441542893648148 foxtail-millet,TJK,-0.4736505150794983 foxtail-millet,TKM,7.5 foxtail-millet,TUN,-0.0 -foxtail-millet,TUR,-1.2562962174415588 +foxtail-millet,TUR,-1.2636922001838684 foxtail-millet,TWN,-7.5 foxtail-millet,TZA,-0.5091361999511719 foxtail-millet,UGA,-0.4788183271884918 @@ -2436,40 +2436,40 @@ foxtail-millet,URY,-0.0 foxtail-millet,USA,-0.43647652864456177 foxtail-millet,UZB,-0.800752580165863 foxtail-millet,VNM,2.965010344982147 -foxtail-millet,YEM,-0.48086604475975037 +foxtail-millet,YEM,-0.43088874220848083 foxtail-millet,ZAF,-0.3926069587469101 foxtail-millet,ZMB,-0.40837621688842773 -foxtail-millet,ZWE,0.03465218981727958 +foxtail-millet,ZWE,0.033776041585952044 gram,AFG,-0.7352392077445984 gram,AGO,-0.9086675047874451 -gram,ALB,-0.16714755538851023 +gram,ALB,-1.7646665275096893 gram,ARE,-0.0 -gram,ARG,-0.06975636258721352 +gram,ARG,-0.09446566924452782 gram,ARM,-7.5 -gram,AUS,1.0015109777450562 +gram,AUS,0.43833449482917786 gram,AUT,-0.2997494637966156 -gram,AZE,0.5424147397279739 +gram,AZE,0.5882295817136765 gram,BDI,-0.8195981085300446 gram,BEL,3.75 gram,BEN,-0.5254355520009995 gram,BFA,-0.9089029431343079 -gram,BGD,0.31519656628370285 +gram,BGD,-0.15886353701353073 gram,BGR,0.014710906893014908 gram,BHS,-0.07298947125673294 gram,BIH,3.988387569785118 gram,BLR,3.75 gram,BLZ,-0.45643289387226105 gram,BOL,7.5 -gram,BRA,-0.3670671880245209 +gram,BRA,-0.3818807005882263 gram,BRB,0.571044921875 gram,BRN,0.0 -gram,BTN,-0.3492923080921173 -gram,BWA,-0.9831105470657349 +gram,BTN,-0.35242418944835663 +gram,BWA,-0.9835602641105652 gram,CAF,-0.5411877334117889 gram,CAN,-7.5 gram,CHE,2.3142648339271545 gram,CHL,0.13134198263287544 -gram,CHN,0.24396827071905136 +gram,CHN,0.22759974002838135 gram,CIV,-0.6746896505355835 gram,CMR,-0.5934893488883972 gram,COD,-0.8777584731578827 @@ -2479,7 +2479,7 @@ gram,COM,-0.583865225315094 gram,CPV,-1.1290497183799744 gram,CRI,-0.9817821383476257 gram,CUB,-0.39652735739946365 -gram,CYP,3.4067196547985077 +gram,CYP,3.52707639336586 gram,CZE,-0.0 gram,DEU,-0.0 gram,DJI,-0.3630117177963257 @@ -2487,7 +2487,7 @@ gram,DNK,-0.0 gram,DOM,-0.0 gram,DZA,-0.0 gram,ECU,-0.5539997071027756 -gram,EGY,0.9314851462841034 +gram,EGY,0.3905002176761627 gram,ERI,-0.8355773091316223 gram,ESP,0.6354125142097473 gram,EST,-0.0 @@ -2503,7 +2503,7 @@ gram,GIN,-0.5684935450553894 gram,GMB,-0.8199613094329834 gram,GNB,-0.9783129990100861 gram,GNQ,7.5 -gram,GRC,-2.390412926673889 +gram,GRC,-2.3958054780960083 gram,GRD,0.21890564262866974 gram,GTM,-0.3826095759868622 gram,GUF,7.5 @@ -2520,7 +2520,7 @@ gram,IRQ,7.5 gram,ISR,-2.724852204322815 gram,ITA,0.07567140460014343 gram,JAM,0.2140841782093048 -gram,JOR,-1.1237521171569824 +gram,JOR,-0.862272322177887 gram,JPN,0.4757440686225891 gram,KAZ,-7.5 gram,KEN,0.24799227714538574 @@ -2536,23 +2536,23 @@ gram,LSO,-0.8371582329273224 gram,LTU,-0.0 gram,LUX,-7.5 gram,LVA,-0.0 -gram,MAR,-0.10505297780036926 +gram,MAR,-0.5824550688266754 gram,MDA,-3.997288227081299 gram,MDG,-0.271031953394413 gram,MEX,-0.6912840604782104 -gram,MKD,0.8599812984466553 +gram,MKD,0.8631795048713684 gram,MLI,-0.6272072792053223 -gram,MMR,-0.45634816586971283 +gram,MMR,-0.48091013729572296 gram,MNE,1.1400986909866333 gram,MNG,0.0 gram,MOZ,-0.8375726342201233 -gram,MRT,-0.452293798327446 +gram,MRT,-0.6060486733913422 gram,MWI,3.5212797224521637 gram,MYS,3.748319522361271 gram,NAM,-0.22467626631259918 gram,NER,-1.1097092628479004 gram,NGA,-0.5940097570419312 -gram,NIC,3.445108860731125 +gram,NIC,3.470130354166031 gram,NLD,-3.75 gram,NOR,-0.17690157890319824 gram,NPL,0.21470989286899567 @@ -2571,7 +2571,7 @@ gram,PSE,-1.772336184978485 gram,ROU,-3.917208656668663 gram,RUS,-0.817993700504303 gram,RWA,-0.7515986561775208 -gram,SAU,1.2217034101486206 +gram,SAU,1.4298428893089294 gram,SDN,-0.5838717818260193 gram,SEN,-0.03980962559580803 gram,SLB,-0.27040238678455353 @@ -2589,7 +2589,7 @@ gram,SYR,-0.0 gram,TCD,-0.880967527627945 gram,TGO,-0.8279393315315247 gram,THA,-0.4950703978538513 -gram,TJK,0.002520691603422165 +gram,TJK,-0.029020902700722218 gram,TKM,-0.33089950680732727 gram,TLS,3.7140812650322914 gram,TTO,0.3565933406352997 @@ -2598,551 +2598,551 @@ gram,TUR,0.4544287174940109 gram,TWN,0.36415984481573105 gram,TZA,-0.29815277457237244 gram,UGA,-0.6347544193267822 -gram,UKR,-0.011923368088901043 +gram,UKR,-0.0 gram,URY,0.2287382334470749 -gram,USA,0.07565681636333466 +gram,USA,0.13988366723060608 gram,UZB,-0.26945221424102783 gram,VEN,-0.4562990218400955 gram,VNM,3.5672258734703064 -gram,YEM,-1.1685735285282135 -gram,ZAF,-0.9054299592971802 +gram,YEM,-1.0763653218746185 +gram,ZAF,-0.7950431108474731 gram,ZMB,-0.7854363322257996 gram,ZWE,-1.033947467803955 groundnut,AFG,7.5 -groundnut,AGO,-1.3191763162612915 -groundnut,ARG,-1.075249969959259 -groundnut,ARM,3.410112828016281 -groundnut,AUS,-1.631212294101715 -groundnut,AZE,-0.8784943222999573 -groundnut,BDI,-1.3486819863319397 -groundnut,BEN,-1.3061746954917908 -groundnut,BFA,-1.2956534028053284 -groundnut,BGD,-0.9790521860122681 +groundnut,AGO,-1.3171497583389282 +groundnut,ARG,-1.057981252670288 +groundnut,ARM,3.417457401752472 +groundnut,AUS,-1.6080958843231201 +groundnut,AZE,-0.6536515951156616 +groundnut,BDI,-1.3467984795570374 +groundnut,BEN,-1.3025845289230347 +groundnut,BFA,-1.2928825616836548 +groundnut,BGD,-1.0977042019367218 groundnut,BGR,-0.0 -groundnut,BLZ,-2.075233221054077 -groundnut,BOL,-0.7756180763244629 -groundnut,BRA,-1.337128460407257 +groundnut,BLZ,-2.070308566093445 +groundnut,BOL,-0.7700313925743103 +groundnut,BRA,-1.3248827457427979 groundnut,BRB,-0.0 groundnut,BRN,-0.0 -groundnut,BTN,0.14171631075441837 -groundnut,BWA,-1.2668901681900024 -groundnut,CAF,-1.167829990386963 +groundnut,BTN,0.23969534784555435 +groundnut,BWA,-1.2642889022827148 +groundnut,CAF,-1.1620892882347107 groundnut,CAN,-0.0 -groundnut,CHN,-3.5426836013793945 -groundnut,CIV,-0.4564383029937744 -groundnut,CMR,-1.2386382222175598 -groundnut,COD,-1.3203104138374329 -groundnut,COG,-1.3435391187667847 -groundnut,COL,-1.1125048398971558 -groundnut,COM,-1.291505753993988 -groundnut,CRI,-1.2076902985572815 -groundnut,CUB,-1.2498579621315002 -groundnut,DOM,-1.4607166647911072 -groundnut,DZA,0.18784833326935768 -groundnut,ECU,-2.249542236328125 -groundnut,EGY,-5.8836236000061035 -groundnut,ERI,-1.3211252689361572 -groundnut,ETH,-1.1839492321014404 -groundnut,FJI,-1.279995322227478 -groundnut,GAB,-1.2965527772903442 -groundnut,GEO,-1.1733970642089844 -groundnut,GHA,-1.0598307251930237 -groundnut,GIN,-0.08361957967281342 -groundnut,GMB,-0.5951736867427826 -groundnut,GNB,-1.2546559572219849 +groundnut,CHN,-3.521635055541992 +groundnut,CIV,-0.4513445198535919 +groundnut,CMR,-1.2346264719963074 +groundnut,COD,-1.3175442218780518 +groundnut,COG,-1.3421473503112793 +groundnut,COL,-1.1049619913101196 +groundnut,COM,-1.2886549830436707 +groundnut,CRI,-1.202875554561615 +groundnut,CUB,-1.2453132271766663 +groundnut,DOM,-1.4535234570503235 +groundnut,DZA,0.32429374754428864 +groundnut,ECU,-2.2455813884735107 +groundnut,EGY,-5.795408010482788 +groundnut,ERI,-1.3193086981773376 +groundnut,ETH,-1.1789480447769165 +groundnut,FJI,-1.2770574688911438 +groundnut,GAB,-1.293135643005371 +groundnut,GEO,-1.1723569631576538 +groundnut,GHA,-1.0531870126724243 +groundnut,GIN,-0.07980263605713844 +groundnut,GMB,-0.5921475887298584 +groundnut,GNB,-1.2507541179656982 groundnut,GNQ,0.0 -groundnut,GRC,-3.2183947563171387 -groundnut,GTM,3.372254729270935 +groundnut,GRC,-3.47408664226532 +groundnut,GTM,3.3826964497566223 groundnut,GUF,-0.0 -groundnut,GUY,-1.2508873343467712 -groundnut,HND,-1.3781964778900146 -groundnut,HTI,-1.3748225569725037 -groundnut,IDN,-1.9952715635299683 -groundnut,IND,-0.9379137754440308 -groundnut,IRN,-1.0678171515464783 -groundnut,IRQ,-0.8352381885051727 -groundnut,ISR,-5.357910394668579 -groundnut,JAM,-2.525640845298767 -groundnut,JOR,-1.076873540878296 -groundnut,JPN,-4.375162839889526 +groundnut,GUY,-1.244444727897644 +groundnut,HND,-1.3772854208946228 +groundnut,HTI,-1.373752772808075 +groundnut,IDN,-1.9885778427124023 +groundnut,IND,-0.9301939904689789 +groundnut,IRN,-1.0587665438652039 +groundnut,IRQ,-0.8314546346664429 +groundnut,ISR,-5.35977840423584 +groundnut,JAM,-2.51817524433136 +groundnut,JOR,-0.7004044950008392 +groundnut,JPN,-4.362792491912842 groundnut,KAZ,7.5 -groundnut,KEN,-0.9415464401245117 -groundnut,KGZ,-0.9406677782535553 -groundnut,KHM,-1.2579824924468994 -groundnut,KOR,-0.907129555940628 -groundnut,LAO,-1.0170224905014038 -groundnut,LBN,-0.6553297638893127 -groundnut,LBR,-1.3528032898902893 -groundnut,LBY,-1.7707195281982422 -groundnut,LKA,-1.3640414476394653 +groundnut,KEN,-0.9381304383277893 +groundnut,KGZ,-0.927876204252243 +groundnut,KHM,-1.25343257188797 +groundnut,KOR,-0.8973130285739899 +groundnut,LAO,-1.0053772032260895 +groundnut,LBN,-0.6392115950584412 +groundnut,LBR,-1.3507665991783142 +groundnut,LBY,-1.1573235988616943 +groundnut,LKA,-1.356644868850708 groundnut,LSO,7.5 -groundnut,MAR,-2.6942797899246216 -groundnut,MDG,-1.3202917575836182 -groundnut,MEX,-0.7945753335952759 -groundnut,MLI,-0.37598279118537903 -groundnut,MMR,-1.2170788049697876 -groundnut,MOZ,-0.28294795751571655 -groundnut,MRT,-1.280310869216919 -groundnut,MUS,-4.1118175983428955 -groundnut,MWI,3.5093905478715897 -groundnut,MYS,-1.1592319011688232 -groundnut,NAM,-1.3332254886627197 -groundnut,NER,-0.14936219155788422 -groundnut,NGA,-1.2799825072288513 -groundnut,NIC,-0.9776860475540161 -groundnut,NPL,-0.7891352772712708 +groundnut,MAR,-2.776275157928467 +groundnut,MDG,-1.3171902894973755 +groundnut,MEX,-0.7903319597244263 +groundnut,MLI,-0.37300197780132294 +groundnut,MMR,-1.211004376411438 +groundnut,MOZ,-0.28193944692611694 +groundnut,MRT,-1.2781457901000977 +groundnut,MUS,-4.1034464836120605 +groundnut,MWI,3.5109453946352005 +groundnut,MYS,-1.1537380814552307 +groundnut,NAM,-1.3323954343795776 +groundnut,NER,-0.1484038084745407 +groundnut,NGA,-1.2752848267555237 +groundnut,NIC,-0.21345052123069763 +groundnut,NPL,-0.7753830254077911 groundnut,OMN,-0.0 -groundnut,PAK,-1.0813229084014893 +groundnut,PAK,-1.0750184059143066 groundnut,PAN,-0.0 -groundnut,PER,-0.9890295267105103 -groundnut,PHL,-0.7525337338447571 -groundnut,PNG,-1.3029815554618835 -groundnut,PRY,-0.47312304377555847 +groundnut,PER,-0.7880614399909973 +groundnut,PHL,-0.7481889426708221 +groundnut,PNG,-1.3001084327697754 +groundnut,PRY,-0.469915434718132 groundnut,PSE,-7.5 -groundnut,RWA,-0.5210429131984711 -groundnut,SAU,-0.6189393103122711 -groundnut,SDN,-1.3498077988624573 -groundnut,SEN,-0.3498563766479492 -groundnut,SLE,-0.3736312687397003 +groundnut,RWA,-0.5198148190975189 +groundnut,SAU,-0.5547128617763519 +groundnut,SDN,-1.3482331037521362 +groundnut,SEN,-0.34632250666618347 +groundnut,SLE,-0.3681301325559616 groundnut,SLV,7.5 -groundnut,SOM,-1.241756558418274 -groundnut,SSD,-1.2588053941726685 -groundnut,SUR,-2.9211336374282837 -groundnut,SWZ,-1.3404398560523987 -groundnut,SYR,3.4543608725070953 -groundnut,TCD,-0.49569642543792725 -groundnut,TGO,-0.2995670288801193 -groundnut,THA,-2.0410284996032715 -groundnut,TJK,-0.8012508749961853 -groundnut,TKM,-0.7691502571105957 -groundnut,TLS,3.7271588388830423 +groundnut,SOM,-1.2388640642166138 +groundnut,SSD,-1.256217896938324 +groundnut,SUR,-2.9160122871398926 +groundnut,SWZ,-1.339042067527771 +groundnut,SYR,3.461653918027878 +groundnut,TCD,-0.4930061399936676 +groundnut,TGO,-0.2968931645154953 +groundnut,THA,-2.0319466590881348 +groundnut,TJK,-0.7890303730964661 +groundnut,TKM,-0.7562479972839355 +groundnut,TLS,3.7291724365204573 groundnut,TUN,-0.0 -groundnut,TUR,-3.5087828636169434 -groundnut,TWN,-0.9948864281177521 -groundnut,TZA,-0.21969842910766602 -groundnut,UGA,-1.3471382856369019 -groundnut,URY,-1.3271565437316895 -groundnut,USA,-1.4485479593276978 +groundnut,TUR,-3.5038957595825195 +groundnut,TWN,-0.9830189645290375 +groundnut,TZA,-0.21714459359645844 +groundnut,UGA,-1.3457993268966675 +groundnut,URY,-1.3253753781318665 +groundnut,USA,-1.4142634868621826 groundnut,UZB,-7.5 -groundnut,VEN,-0.8497638404369354 -groundnut,VNM,-2.0668810606002808 -groundnut,VUT,-1.2147810459136963 -groundnut,YEM,-1.2930192947387695 -groundnut,ZAF,-0.44431328773498535 -groundnut,ZMB,-0.584925651550293 -groundnut,ZWE,-0.45885011553764343 -maize,AFG,3.455686181783676 -maize,AGO,-0.8852007985115051 -maize,ALB,-1.3717151284217834 -maize,ARE,2.0169368386268616 -maize,ARG,-0.3068256974220276 -maize,ARM,-0.4349454343318939 -maize,ATG,-1.0997954607009888 +groundnut,VEN,-0.832469254732132 +groundnut,VNM,-2.0565937757492065 +groundnut,VUT,-1.2093496918678284 +groundnut,YEM,-1.1971211433410645 +groundnut,ZAF,-0.411416232585907 +groundnut,ZMB,-0.5827446579933167 +groundnut,ZWE,-0.4572310447692871 +maize,AFG,3.4593666195869446 +maize,AGO,-0.8864012360572815 +maize,ALB,-0.5176059752702713 +maize,ARE,2.0679168701171875 +maize,ARG,-0.2930562198162079 +maize,ARM,-0.42587652802467346 +maize,ATG,-1.0962660312652588 maize,AUS,7.5 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-sugarbeet,MNE,-1.214330792427063 +sugarbeet,MNE,-1.118463933467865 sugarbeet,MNG,7.5 -sugarbeet,NLD,-2.771073579788208 -sugarbeet,NOR,4.008627563714981 +sugarbeet,NLD,-2.732706904411316 +sugarbeet,NOR,4.044773310422897 sugarbeet,PAK,7.5 -sugarbeet,POL,-0.6702487170696259 -sugarbeet,PRT,0.5573842823505402 -sugarbeet,PSE,3.293665826320648 -sugarbeet,ROU,-0.5220624953508377 -sugarbeet,RUS,-1.0268279910087585 -sugarbeet,SRB,-0.9013720452785492 -sugarbeet,SVK,-0.24754204601049423 -sugarbeet,SVN,-0.5583831816911697 -sugarbeet,SWE,-0.5472275614738464 +sugarbeet,POL,-0.634486973285675 +sugarbeet,PRT,0.621670737862587 +sugarbeet,PSE,3.307444602251053 +sugarbeet,ROU,-0.4991711378097534 +sugarbeet,RUS,-1.0088413953781128 +sugarbeet,SRB,-0.8408879041671753 +sugarbeet,SVK,-0.5831716358661652 +sugarbeet,SVN,-0.6871457993984222 +sugarbeet,SWE,-0.5028652548789978 sugarbeet,SYR,7.5 sugarbeet,TJK,7.5 -sugarbeet,TKM,-1.7700532674789429 -sugarbeet,TUN,-1.0292125642299652 -sugarbeet,TUR,-1.8403560519218445 -sugarbeet,UKR,-1.3022127151489258 -sugarbeet,USA,-2.0194687843322754 -sugarbeet,UZB,-0.30246801674366 +sugarbeet,TKM,-1.766564965248108 +sugarbeet,TUN,-0.9134003520011902 +sugarbeet,TUR,-1.8057040572166443 +sugarbeet,UKR,-1.634295642375946 +sugarbeet,USA,-1.9338568449020386 +sugarbeet,UZB,-0.29010504484176636 sugarbeet,VNM,7.5 sugarcane,AFG,-2.3949992656707764 -sugarcane,AGO,-2.2606115341186523 +sugarcane,AGO,-2.2756917476654053 sugarcane,ARE,-0.0 sugarcane,ARG,-2.882798194885254 -sugarcane,AUS,-4.211271286010742 +sugarcane,AUS,-3.718235731124878 sugarcane,BDI,-2.1910054683685303 -sugarcane,BEN,-2.464614510536194 +sugarcane,BEN,-4.953248143196106 sugarcane,BFA,-2.5402884483337402 -sugarcane,BGD,-2.526457905769348 +sugarcane,BGD,-2.898882031440735 sugarcane,BHS,-2.3660157918930054 sugarcane,BLZ,-1.9782506227493286 sugarcane,BOL,-4.803053379058838 -sugarcane,BRA,-2.075727701187134 +sugarcane,BRA,-2.0856943130493164 sugarcane,BRB,-0.0 sugarcane,BRN,-0.0 sugarcane,BTN,-7.5 -sugarcane,BWA,-2.0475070476531982 +sugarcane,BWA,-2.047506809234619 sugarcane,CAF,-2.293934941291809 sugarcane,CHN,-7.5 sugarcane,CIV,-2.394665837287903 @@ -4881,20 +4881,20 @@ sugarcane,CMR,-2.151980400085449 sugarcane,COD,-2.3136093616485596 sugarcane,COG,-2.3536309003829956 sugarcane,COL,-2.7655266523361206 -sugarcane,CPV,-2.3767861127853394 +sugarcane,CPV,-7.5 sugarcane,CRI,-2.2228267192840576 sugarcane,CUB,-2.6799018383026123 sugarcane,DJI,-0.0 -sugarcane,DOM,-3.624391198158264 -sugarcane,DZA,2.628586173057556 +sugarcane,DOM,-3.675437092781067 +sugarcane,DZA,2.60935378074646 sugarcane,ECU,-2.44791316986084 -sugarcane,EGY,-4.866864442825317 +sugarcane,EGY,-4.566298961639404 sugarcane,ERI,-2.2367587089538574 sugarcane,ETH,-4.6970813274383545 sugarcane,FJI,-2.1166298389434814 sugarcane,GAB,-2.2554919719696045 sugarcane,GHA,-2.465382218360901 -sugarcane,GIN,-2.415286064147949 +sugarcane,GIN,-4.9308390617370605 sugarcane,GMB,-0.0 sugarcane,GNB,-2.3483564853668213 sugarcane,GNQ,7.5 @@ -4904,38 +4904,38 @@ sugarcane,GUF,7.5 sugarcane,GUY,-3.093653440475464 sugarcane,HND,-2.8159632682800293 sugarcane,HTI,-2.6820844411849976 -sugarcane,IDN,-2.4946975708007812 -sugarcane,IND,-2.708992838859558 +sugarcane,IDN,-2.49464213848114 +sugarcane,IND,-2.7071582078933716 sugarcane,IRN,-2.70224666595459 sugarcane,IRQ,-2.2859115600585938 sugarcane,ISR,-0.0 sugarcane,JAM,-2.0053582787513733 -sugarcane,JPN,-7.338368892669678 -sugarcane,KEN,-2.1735674142837524 +sugarcane,JPN,-7.33183217048645 +sugarcane,KEN,-2.202704906463623 sugarcane,KHM,-2.46723735332489 sugarcane,LAO,-2.550755500793457 sugarcane,LBR,-2.3199148178100586 sugarcane,LKA,-1.4503036141395569 sugarcane,LSO,-2.2442567348480225 -sugarcane,MAR,-3.7259137630462646 +sugarcane,MAR,-4.293485403060913 sugarcane,MDG,-2.365809679031372 sugarcane,MEX,-2.6775386333465576 sugarcane,MLI,-2.453232765197754 sugarcane,MMR,-2.8034855127334595 -sugarcane,MOZ,-2.199744462966919 +sugarcane,MOZ,-2.19974422454834 sugarcane,MRT,-2.3416426181793213 -sugarcane,MUS,-2.878471851348877 -sugarcane,MWI,-2.3566224575042725 +sugarcane,MUS,-2.878471612930298 +sugarcane,MWI,-2.3566222190856934 sugarcane,MYS,-3.6528178453445435 -sugarcane,NAM,-2.0361316204071045 -sugarcane,NER,-7.276106119155884 +sugarcane,NAM,-2.0361313819885254 +sugarcane,NER,-7.5 sugarcane,NGA,-2.3795244693756104 -sugarcane,NIC,-1.8018004298210144 +sugarcane,NIC,-1.1951013803482056 sugarcane,NPL,-2.3074162006378174 -sugarcane,OMN,-1.6449772715568542 +sugarcane,OMN,-2.2764881253242493 sugarcane,PAK,-2.865852117538452 sugarcane,PAN,-2.7833728790283203 -sugarcane,PER,-2.5865120887756348 +sugarcane,PER,-2.04551237821579 sugarcane,PHL,-2.7199974060058594 sugarcane,PNG,-2.514993667602539 sugarcane,PRY,-2.0175960063934326 @@ -4944,7 +4944,7 @@ sugarcane,RWA,-4.039017677307129 sugarcane,SDN,-2.3614293336868286 sugarcane,SEN,-1.9679336547851562 sugarcane,SLE,-4.9593788385391235 -sugarcane,SLV,-2.9893568754196167 +sugarcane,SLV,-3.139855980873108 sugarcane,SOM,-2.1358535289764404 sugarcane,SSD,-2.14503812789917 sugarcane,SUR,-2.7716208696365356 @@ -4955,38 +4955,38 @@ sugarcane,THA,-1.7725938558578491 sugarcane,TKM,7.5 sugarcane,TLS,-3.4707822799682617 sugarcane,TWN,-2.515976309776306 -sugarcane,TZA,-2.1785526275634766 -sugarcane,UGA,-3.9726401567459106 +sugarcane,TZA,-2.1527003049850464 +sugarcane,UGA,-3.976483941078186 sugarcane,URY,-2.958683133125305 sugarcane,USA,-3.307131767272949 sugarcane,VEN,-2.5730087757110596 sugarcane,VNM,-2.6067190170288086 -sugarcane,ZAF,-2.2448601722717285 +sugarcane,ZAF,-2.20822811126709 sugarcane,ZMB,-2.3346107006073 sugarcane,ZWE,-7.5 sunflower,AFG,-0.3901115357875824 sunflower,AGO,-0.5811295509338379 -sunflower,ALB,-1.3784627318382263 +sunflower,ALB,-1.4421820640563965 sunflower,ARG,-0.2512015029788017 sunflower,ARM,0.18755126744508743 sunflower,AUS,7.5 -sunflower,AUT,-0.273326151072979 -sunflower,AZE,-0.8795689642429352 +sunflower,AUT,-0.3094194605946541 +sunflower,AZE,-0.7285646796226501 sunflower,BDI,-0.47099801898002625 sunflower,BEL,-0.012950197793543339 sunflower,BGD,3.5095990747213364 -sunflower,BGR,-0.14135482162237167 +sunflower,BGR,-0.12068775296211243 sunflower,BIH,-0.07370292767882347 -sunflower,BLR,-0.3426509499549866 +sunflower,BLR,-0.34512490034103394 sunflower,BOL,0.06139788776636124 sunflower,BRA,0.028978029265999794 sunflower,BTN,7.5 -sunflower,BWA,-0.6156436204910278 +sunflower,BWA,-0.6159760355949402 sunflower,CAF,7.5 -sunflower,CAN,-0.10777654498815536 +sunflower,CAN,-0.14542526751756668 sunflower,CHE,-1.0816128849983215 sunflower,CHL,-0.4541497528553009 -sunflower,CHN,-1.3617665767669678 +sunflower,CHN,-1.344519853591919 sunflower,COD,-0.3954220712184906 sunflower,COG,-0.6202561855316162 sunflower,COL,7.5 @@ -4995,26 +4995,26 @@ sunflower,DEU,-0.03251445759087801 sunflower,DNK,-0.0 sunflower,DZA,0.07130832597613335 sunflower,ECU,-0.36056022346019745 -sunflower,EGY,-0.33548668026924133 +sunflower,EGY,-0.28044968843460083 sunflower,ERI,-0.0 -sunflower,ESP,-0.14190415292978287 +sunflower,ESP,-0.09586328640580177 sunflower,EST,-0.22311664372682571 sunflower,ETH,-0.21693839132785797 sunflower,FIN,-0.0 -sunflower,FRA,-0.27413320541381836 +sunflower,FRA,-0.23136338591575623 sunflower,GEO,-0.1343516707420349 -sunflower,GRC,-0.49574409425258636 +sunflower,GRC,-0.47191759943962097 sunflower,GUF,-0.0 sunflower,GUY,-0.0 sunflower,HRV,-4.0307208597660065 -sunflower,HUN,0.24583354778587818 +sunflower,HUN,-1.5372393727302551 sunflower,IND,-0.38811564445495605 sunflower,IRN,-0.4929926246404648 -sunflower,IRQ,0.00202338513918221 +sunflower,IRQ,-0.005212508141994476 sunflower,ISR,-3.577317476272583 -sunflower,ITA,-0.11313702911138535 +sunflower,ITA,-0.2249818593263626 sunflower,JOR,7.5 -sunflower,KAZ,-0.1184820719063282 +sunflower,KAZ,0.011884578503668308 sunflower,KEN,-0.06812488846480846 sunflower,KGZ,-0.3999648541212082 sunflower,KHM,3.496968775987625 @@ -5024,11 +5024,11 @@ sunflower,LSO,0.0011492669582366943 sunflower,LTU,-0.3288004398345947 sunflower,LUX,-0.3222738206386566 sunflower,LVA,3.6635011732578278 -sunflower,MAR,-0.9839224517345428 -sunflower,MDA,0.18412020802497864 +sunflower,MAR,-0.9989871978759766 +sunflower,MDA,-0.033004900033120066 sunflower,MEX,0.08509547635912895 -sunflower,MKD,-0.1180204302072525 -sunflower,MMR,-0.4749790132045746 +sunflower,MKD,-0.05154420807957649 +sunflower,MMR,-0.474979043006897 sunflower,MNE,3.7419697120785713 sunflower,MNG,-0.22355228662490845 sunflower,MOZ,0.12690520286560059 @@ -5043,15 +5043,15 @@ sunflower,POL,3.6944689899683 sunflower,PRT,-0.12797577306628227 sunflower,PRY,-0.41398580372333527 sunflower,PSE,0.3088647276163101 -sunflower,ROU,0.03187793120741844 +sunflower,ROU,0.023025332018733025 sunflower,RUS,0.09683644026517868 sunflower,RWA,-0.5444460809230804 -sunflower,SDN,-0.5817890763282776 +sunflower,SDN,-0.5909189581871033 sunflower,SOM,-0.0 -sunflower,SRB,0.5187985226511955 +sunflower,SRB,0.5367562100291252 sunflower,SSD,-0.3364241123199463 sunflower,SUR,-0.0 -sunflower,SVK,0.17326292768120766 +sunflower,SVK,-0.22962512075901031 sunflower,SVN,0.1256725862622261 sunflower,SWZ,-0.32262498140335083 sunflower,SYR,-0.3976607322692871 @@ -5064,286 +5064,286 @@ sunflower,TUR,-0.5904028415679932 sunflower,TWN,0.7117132544517517 sunflower,TZA,0.0535622276365757 sunflower,UGA,-0.4883206635713577 -sunflower,UKR,-0.35528774559497833 +sunflower,UKR,-0.43149857223033905 sunflower,URY,-0.27959302067756653 -sunflower,USA,-0.00981269683688879 +sunflower,USA,0.012348195537924767 sunflower,UZB,-0.6423380374908447 sunflower,VEN,-0.5422933995723724 sunflower,VNM,-0.0 -sunflower,ZAF,-0.2700006067752838 +sunflower,ZAF,-0.16495691239833832 sunflower,ZMB,-0.6156052947044373 sunflower,ZWE,-0.098918616771698 -sweet-potato,AFG,-2.991481304168701 -sweet-potato,AGO,-3.585181951522827 -sweet-potato,ALB,-3.1089872121810913 -sweet-potato,ARE,-2.486806273460388 -sweet-potato,ARG,-3.607067584991455 -sweet-potato,ARM,-2.788231134414673 -sweet-potato,ATG,-4.393624782562256 +sweet-potato,AFG,-2.985364317893982 +sweet-potato,AGO,-3.588388204574585 +sweet-potato,ALB,-2.863768458366394 +sweet-potato,ARE,-2.477710008621216 +sweet-potato,ARG,-3.5867621898651123 +sweet-potato,ARM,-2.780849575996399 +sweet-potato,ATG,-4.392462730407715 sweet-potato,AUS,7.5 -sweet-potato,AUT,-2.7696714401245117 -sweet-potato,AZE,-3.2415637969970703 -sweet-potato,BDI,-1.154886245727539 -sweet-potato,BEL,-1.8922550082206726 -sweet-potato,BEN,-1.651827096939087 -sweet-potato,BFA,-3.577608346939087 -sweet-potato,BGD,-0.4940282553434372 -sweet-potato,BGR,-2.9617868661880493 -sweet-potato,BHS,-3.834680438041687 -sweet-potato,BIH,2.076244831085205 -sweet-potato,BLR,-5.288215160369873 -sweet-potato,BLZ,-3.588991403579712 -sweet-potato,BOL,-0.7515311241149902 -sweet-potato,BRA,-2.629443883895874 +sweet-potato,AUT,-2.759915590286255 +sweet-potato,AZE,-3.200307011604309 +sweet-potato,BDI,-1.1513694524765015 +sweet-potato,BEL,-1.875126302242279 +sweet-potato,BEN,-2.1397756934165955 +sweet-potato,BFA,-3.5748777389526367 +sweet-potato,BGD,-0.7245415300130844 +sweet-potato,BGR,-2.9531651735305786 +sweet-potato,BHS,-3.8333626985549927 +sweet-potato,BIH,2.078806161880493 +sweet-potato,BLR,-5.284373879432678 +sweet-potato,BLZ,-3.5842210054397583 +sweet-potato,BOL,-0.7738971710205078 +sweet-potato,BRA,-2.6456756591796875 sweet-potato,BRB,-7.5 sweet-potato,BRN,-7.5 sweet-potato,BTN,7.5 sweet-potato,BWA,-0.0 -sweet-potato,CAF,-3.8880326747894287 -sweet-potato,CAN,-3.1307886838912964 -sweet-potato,CHE,-2.1317754983901978 +sweet-potato,CAF,-3.887585759162903 +sweet-potato,CAN,-3.1171178817749023 +sweet-potato,CHE,-2.116966485977173 sweet-potato,CHL,-7.5 sweet-potato,CHN,-7.5 -sweet-potato,CIV,-0.5126045346260071 -sweet-potato,CMR,-3.449243664741516 -sweet-potato,COD,-3.6700280904769897 -sweet-potato,COG,-3.4940396547317505 -sweet-potato,COL,-3.262499213218689 -sweet-potato,COM,-3.7716764211654663 +sweet-potato,CIV,-0.5119131207466125 +sweet-potato,CMR,-3.446287512779236 +sweet-potato,COD,-3.668230414390564 +sweet-potato,COG,-3.491325616836548 +sweet-potato,COL,-3.254835844039917 +sweet-potato,COM,-3.7704436779022217 sweet-potato,CPV,-7.5 -sweet-potato,CRI,3.1104167103767395 -sweet-potato,CUB,-3.4293575286865234 -sweet-potato,CYP,-5.222730398178101 -sweet-potato,CZE,-2.366992712020874 -sweet-potato,DEU,-1.8211749792099 -sweet-potato,DNK,-2.005644142627716 -sweet-potato,DOM,-2.8651424646377563 -sweet-potato,DZA,-2.779192805290222 -sweet-potato,ECU,-0.09098181501030922 -sweet-potato,EGY,-3.18618381023407 -sweet-potato,ERI,1.7972681522369385 +sweet-potato,CRI,3.1132550835609436 +sweet-potato,CUB,-3.423911690711975 +sweet-potato,CYP,-5.017848014831543 +sweet-potato,CZE,-2.3541070222854614 +sweet-potato,DEU,-1.8033985495567322 +sweet-potato,DNK,-1.9892099499702454 +sweet-potato,DOM,-2.9093624353408813 +sweet-potato,DZA,-2.6932742595672607 +sweet-potato,ECU,-0.08588851429522038 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-wheat,LBN,-0.4684416353702545 -wheat,LBY,-0.9928775429725647 -wheat,LSO,0.01541497721336782 -wheat,LTU,-0.37833620607852936 -wheat,LUX,-0.3504319190979004 -wheat,LVA,-0.29870377480983734 -wheat,MAR,-0.6169018745422363 -wheat,MDA,-0.05449158791452646 -wheat,MDG,-0.5555359423160553 -wheat,MEX,-0.8163051009178162 -wheat,MKD,-0.23713015019893646 -wheat,MLI,-0.38727493584156036 -wheat,MLT,-1.3568672761321068 -wheat,MMR,-0.6508816480636597 -wheat,MNE,-0.028866415843367577 -wheat,MNG,-0.11128392815589905 -wheat,MOZ,-0.4236850291490555 -wheat,MRT,-0.37622320652008057 -wheat,MWI,3.4199984073638916 -wheat,NAM,-1.1223211288452148 -wheat,NER,-0.2710269093513489 -wheat,NGA,-0.6300567388534546 +wheat,LBN,-0.46113844215869904 +wheat,LBY,-0.5877634882926941 +wheat,LSO,0.018944926327094436 +wheat,LTU,-0.3654216229915619 +wheat,LUX,-0.3333802744746208 +wheat,LVA,-0.28504279255867004 +wheat,MAR,-0.6407093107700348 +wheat,MDA,-3.281911112368107 +wheat,MDG,-0.5506739914417267 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+white-potato,ESP,-7.4624505043029785 +white-potato,EST,-2.4551055431365967 +white-potato,ETH,-0.8870992660522461 +white-potato,FIN,-3.5783214569091797 white-potato,FRA,-7.5 -white-potato,GAB,-7.145329713821411 -white-potato,GBR,-6.978541612625122 -white-potato,GEO,-1.797124981880188 -white-potato,GHA,-7.1560587882995605 -white-potato,GIN,-6.860351800918579 +white-potato,GAB,-7.141624450683594 +white-potato,GBR,-6.86384391784668 +white-potato,GEO,-1.7592663764953613 +white-potato,GHA,-7.1526288986206055 +white-potato,GIN,-6.836480617523193 white-potato,GMB,0.0 -white-potato,GNB,-7.036376237869263 -white-potato,GNQ,-7.04372239112854 +white-potato,GNB,-7.026879072189331 +white-potato,GNQ,-7.034653663635254 white-potato,GRC,-7.5 -white-potato,GRD,-7.003868103027344 -white-potato,GTM,-5.283800363540649 +white-potato,GRD,-6.998309135437012 +white-potato,GTM,-5.201241731643677 white-potato,GUF,-7.5 white-potato,GUY,3.75 white-potato,HND,-7.5 -white-potato,HRV,-5.252739787101746 -white-potato,HTI,-6.981094598770142 -white-potato,HUN,-5.4380292892456055 +white-potato,HRV,-5.217552185058594 +white-potato,HTI,-6.971254825592041 +white-potato,HUN,-7.087100505828857 white-potato,IDN,-7.5 -white-potato,IND,-5.721426963806152 -white-potato,IRL,-4.704069375991821 -white-potato,IRN,-3.726747751235962 -white-potato,IRQ,-7.5 +white-potato,IND,-5.638177871704102 +white-potato,IRL,-4.556267499923706 +white-potato,IRN,-3.6225157976150513 +white-potato,IRQ,-7.441088914871216 white-potato,ISR,-7.5 white-potato,ITA,-7.5 white-potato,JAM,-7.5 white-potato,JOR,-7.5 white-potato,JPN,-7.5 -white-potato,KAZ,-2.6429775953292847 -white-potato,KEN,-1.9880510568618774 -white-potato,KGZ,-3.1627609729766846 -white-potato,KHM,-6.845048427581787 -white-potato,KOR,-6.112677574157715 -white-potato,LAO,0.7128591537475586 +white-potato,KAZ,-2.512453079223633 +white-potato,KEN,-1.9651799201965332 +white-potato,KGZ,-3.1238272190093994 +white-potato,KHM,-6.822843313217163 +white-potato,KOR,-6.06976056098938 +white-potato,LAO,0.744927167892456 white-potato,LBN,-7.5 -white-potato,LBR,-6.4856953620910645 -white-potato,LBY,-6.387524127960205 +white-potato,LBR,-6.44934606552124 +white-potato,LBY,-5.428160190582275 white-potato,LKA,-7.5 -white-potato,LSO,-5.1936609745025635 -white-potato,LTU,-2.3454747200012207 -white-potato,LUX,-6.501452684402466 -white-potato,LVA,-1.469796061515808 -white-potato,MAR,-4.77480936050415 -white-potato,MDA,-0.5749216079711914 -white-potato,MDG,-6.8332860469818115 +white-potato,LSO,-5.115719556808472 +white-potato,LTU,-2.2790311574935913 +white-potato,LUX,-6.411865949630737 +white-potato,LVA,-1.38764888048172 +white-potato,MAR,-4.819385528564453 +white-potato,MDA,-0.81363844871521 +white-potato,MDG,-6.81690526008606 white-potato,MEX,-7.5 -white-potato,MKD,-3.374205231666565 +white-potato,MKD,-3.2531501054763794 white-potato,MLI,-7.5 white-potato,MLT,-7.5 -white-potato,MMR,-6.3627142906188965 -white-potato,MNE,-6.050410747528076 -white-potato,MNG,-2.7470810413360596 +white-potato,MMR,-6.315929174423218 +white-potato,MNE,-5.987045049667358 +white-potato,MNG,-2.7080636024475098 white-potato,MOZ,-7.5 white-potato,MRT,-7.5 white-potato,MUS,-7.5 -white-potato,MWI,1.1931469440460205 -white-potato,MYS,0.7353165149688721 -white-potato,NAM,-6.313289165496826 +white-potato,MWI,1.2300331592559814 +white-potato,MYS,0.7674200534820557 +white-potato,NAM,-6.278165817260742 white-potato,NER,-7.5 -white-potato,NGA,-7.081552505493164 +white-potato,NGA,-7.074794769287109 white-potato,NIC,-7.5 -white-potato,NLD,-5.348926067352295 +white-potato,NLD,-5.281841278076172 white-potato,NOR,-7.5 -white-potato,NPL,-2.9491978883743286 -white-potato,NZL,-4.927463531494141 +white-potato,NPL,-2.9054782390594482 +white-potato,NZL,-4.806726694107056 white-potato,OMN,-7.5 -white-potato,PAK,-2.3766369819641113 +white-potato,PAK,-2.2872602939605713 white-potato,PAN,-7.5 -white-potato,PER,-3.6772572994232178 +white-potato,PER,-3.937328577041626 white-potato,PHL,-7.5 white-potato,PNG,-7.5 -white-potato,POL,-1.2998344898223877 +white-potato,POL,-1.1910698413848877 white-potato,PRI,-7.5 -white-potato,PRT,-4.856859445571899 -white-potato,PRY,-3.5981976985931396 +white-potato,PRT,-4.766546010971069 +white-potato,PRY,-3.574838638305664 white-potato,PSE,-7.5 -white-potato,ROU,-4.347909927368164 -white-potato,RUS,-2.243032217025757 -white-potato,RWA,-1.8845118284225464 +white-potato,ROU,-4.253390789031982 +white-potato,RUS,-2.1999588012695312 +white-potato,RWA,-1.8684542179107666 white-potato,SAU,-7.5 -white-potato,SDN,-6.330223083496094 +white-potato,SDN,-6.287956476211548 white-potato,SEN,-7.5 -white-potato,SLB,-6.304596185684204 -white-potato,SLE,0.3968930244445801 +white-potato,SLB,-6.256526470184326 +white-potato,SLE,0.4096717834472656 white-potato,SLV,0.0 -white-potato,SOM,-6.544100761413574 -white-potato,SRB,-2.9608778953552246 +white-potato,SOM,-6.517856597900391 +white-potato,SRB,-2.8107374906539917 white-potato,SSD,-7.5 white-potato,SUR,-7.5 -white-potato,SVK,-5.108900308609009 -white-potato,SVN,-5.888192892074585 +white-potato,SVK,-5.623281002044678 +white-potato,SVN,-5.785842418670654 white-potato,SWE,-7.5 -white-potato,SWZ,-7.1311492919921875 -white-potato,SYR,-5.786017417907715 -white-potato,TCD,-6.9428582191467285 -white-potato,TGO,-7.07680869102478 -white-potato,THA,-4.557903289794922 -white-potato,TJK,-3.1004536151885986 -white-potato,TKM,-6.421767234802246 -white-potato,TLS,-0.613696813583374 -white-potato,TTO,-7.044182777404785 -white-potato,TUN,-2.8982425928115845 -white-potato,TUR,-5.410451412200928 -white-potato,TWN,-5.71392822265625 -white-potato,TZA,-0.7925076484680176 -white-potato,UGA,-6.982980728149414 -white-potato,UKR,-1.5892049074172974 +white-potato,SWZ,-7.126962423324585 +white-potato,SYR,-5.717710018157959 +white-potato,TCD,-6.933981657028198 +white-potato,TGO,-7.06972336769104 +white-potato,THA,-4.497165203094482 +white-potato,TJK,-3.0708377361297607 +white-potato,TKM,-6.382200717926025 +white-potato,TLS,-0.60957932472229 +white-potato,TTO,-7.033324718475342 +white-potato,TUN,-2.753532648086548 +white-potato,TUR,-5.135773181915283 +white-potato,TWN,-5.633113145828247 +white-potato,TZA,-0.7722519040107727 +white-potato,UGA,-6.97179913520813 +white-potato,UKR,-1.6040884852409363 white-potato,URY,-7.5 -white-potato,USA,-4.881575107574463 -white-potato,UZB,-5.046460151672363 +white-potato,USA,-4.687905788421631 +white-potato,UZB,-4.987804889678955 white-potato,VEN,-7.5 -white-potato,VNM,-6.533275842666626 -white-potato,YEM,-7.5 -white-potato,ZAF,-6.654969930648804 +white-potato,VNM,-6.490125417709351 +white-potato,YEM,-7.476073980331421 +white-potato,ZAF,-6.370171546936035 white-potato,ZMB,-7.5 white-potato,ZWE,-7.5 yam,AFG,-0.0 @@ -6055,13 +6055,13 @@ yam,BDI,-4.5215067863464355 yam,BEN,-1.3934463858604431 yam,BFA,-2.313081979751587 yam,BIH,3.75 -yam,BLZ,-1.7673701643943787 +yam,BLZ,-1.5160371661186218 yam,BOL,-5.210255146026611 yam,BRA,-2.8700002431869507 yam,BRB,-7.5 yam,BRN,-5.240689039230347 -yam,BTN,-2.0861212015151978 -yam,BWA,-3.6083484888076782 +yam,BTN,-2.089253067970276 +yam,BWA,-3.6090399026870728 yam,CAF,-4.071576118469238 yam,CHN,-0.7571600675582886 yam,CIV,-0.7280837893486023 @@ -6074,7 +6074,7 @@ yam,CRI,-7.5 yam,CUB,-2.746282696723938 yam,DOM,-1.9059787392616272 yam,ECU,-5.174973964691162 -yam,EGY,0.33129335939884186 +yam,EGY,-0.55497807264328 yam,ERI,-5.6153175830841064 yam,ETH,-1.8165869116783142 yam,FJI,-7.5 @@ -6118,11 +6118,11 @@ yam,MYS,-2.7936019897460938 yam,NAM,-3.4631645679473877 yam,NER,-5.156688213348389 yam,NGA,-3.9387731552124023 -yam,NIC,2.7137115001678467 +yam,NIC,2.753152072429657 yam,NPL,-5.065113067626953 yam,PAK,-0.0 yam,PAN,-3.2264866828918457 -yam,PER,-3.5559438467025757 +yam,PER,-3.73064386844635 yam,PHL,-1.6469984650611877 yam,PNG,-3.0244327783584595 yam,PRI,-7.5 diff --git a/data/curated/calibration/default/deviation_penalty.yaml b/data/curated/calibration/default/deviation_penalty.yaml index 177119fe..baba7334 100644 --- a/data/curated/calibration/default/deviation_penalty.yaml +++ b/data/curated/calibration/default/deviation_penalty.yaml @@ -14,9 +14,9 @@ components: - grassland - feed l1_costs: - cropland: 1.4668100847398033 - grassland: 0.23756434523520123 - feed: 0.06172639547529331 -iterations: 3 + cropland: 1.464706091072274 + grassland: 0.23438672041352424 + feed: 0.061778539182739414 +iterations: 2 converged: true -final_residual_log_inf: 0.018785337116737355 +final_residual_log_inf: 0.0067152684187542725 diff --git a/data/curated/calibration/default/exogenous_feed.csv b/data/curated/calibration/default/exogenous_feed.csv index dcd11041..0205efed 100644 --- a/data/curated/calibration/default/exogenous_feed.csv +++ b/data/curated/calibration/default/exogenous_feed.csv @@ -99,7 +99,7 @@ TLS,0.010043,0.01037,0.067068 TTO,0.0,0.0,0.00687 TUN,0.0,0.0,1.232322 TUR,0.0,0.0,1.617232 -TWN,1.287958,0.0,0.0 +TWN,1.287218,0.00074,0.0 TZA,0.0,0.0,9.70183 UGA,0.0,0.0,3.548997 URY,0.0,0.0,2.438507 diff --git a/data/curated/calibration/default/food_demand.csv b/data/curated/calibration/default/food_demand.csv index 27f57e44..a50aa9cb 100644 --- a/data/curated/calibration/default/food_demand.csv +++ b/data/curated/calibration/default/food_demand.csv @@ -33,7 +33,7 @@ meat-pig,1.0184832060001492,-1.397727906703949,75.62150779971125 meat-sheep,1.0872487675309466,-0.891366062220186,10.21637425312656 oat,1.0,0.0,1.1316055530789981 olive-oil,1.299325925599697,-1.135268522426486,3.7927503945807075 -onion,0.9917853867045111,2.3257164411770646,283.11940654032514 +onion,0.9917853867078797,2.3257164402233457,283.11940654032514 palm-oil,0.5,36.668308315522154,21.10606127652136 pearl-millet,1.0,0.0,3.234954914529226 phaseolus-bean,1.0,0.0,21.57145358827256 diff --git a/data/curated/calibration/default/grassland_cost.csv b/data/curated/calibration/default/grassland_cost.csv index 23062ba3..fa58aafa 100644 --- a/data/curated/calibration/default/grassland_cost.csv +++ b/data/curated/calibration/default/grassland_cost.csv @@ -1,99 +1,99 @@ country,correction_bnusd_per_mha AFG,-4.0427408296017653e-10 AGO,-0.38177555799484253 -ALB,-0.17037621140480042 +ALB,-0.07620231807231903 ARE,-0.0 ARG,-0.18566030263900757 ARM,-0.0 ATG,-0.0 -AUS,-0.030936773866415024 +AUS,-0.03066573292016983 AUT,9.24111176470177e-10 -AZE,0.8003178536891937 +AZE,0.8143752813339233 BDI,-0.49132993817329407 BEL,0.08877222612500191 -BEN,2.4552853107452393 -BFA,1.1361154317855835 -BGD,4.882810354232788 -BGR,-0.2715888172388077 +BEN,2.497702121734619 +BFA,1.1558977365493774 +BGD,4.958475828170776 +BGR,-0.2710699886083603 BHS,-0.03126266412436962 -BIH,-0.44524990022182465 -BLR,-8.282973240625324e-08 +BIH,-0.4450627416372299 +BLR,-0.0 BLZ,-0.7399452030658722 BOL,-0.6221835613250732 BRA,-0.5207413733005524 -BRB,0.7491881251335144 +BRB,0.7633196711540222 BRN,-3.437714735099462e-08 -BTN,-0.3210534453392029 +BTN,-0.3209189474582672 BWA,-0.0 CAF,-0.44557350873947144 -CAN,-0.21650707721710205 +CAN,-0.21589738130569458 CHE,-0.0 CHL,-0.03965021949261427 -CHN,-6.799676377511332e-09 -CIV,-0.5334059000015259 -CMR,2.3226504921913147 +CHN,-2.285429179238463e-09 +CIV,-0.5311106741428375 +CMR,2.360807180404663 COD,-0.7531084418296814 COG,-0.8099571466445923 COL,-0.6204277873039246 -COM,1.216862440109253 +COM,1.2368063926696777 CPV,-0.0 CRI,-0.04483989253640175 CUB,-0.17085150629281998 -CYP,0.2554362267255783 -CZE,-0.12467751279473305 +CYP,0.25957997143268585 +CZE,-0.12304586917161942 DEU,-0.24095454812049866 DJI,-0.0 -DNK,1.6213496327400208 +DNK,1.649174988269806 DOM,-0.4018055945634842 DZA,-2.098951901174928e-09 ECU,-0.19687405228614807 EGY,-0.0 ERI,-0.04815981723368168 -ESP,-0.020102565176784992 +ESP,-0.018636401742696762 EST,-0.26680906116962433 -ETH,-1.9732169675990008e-08 -FIN,1.3795255422592163 -FJI,-0.8463798761367798 -FRA,1.5480199344608536e-08 -GAB,-0.8217355906963348 +ETH,-3.095637302052978e-09 +FIN,1.4029467701911926 +FJI,-0.8459053635597229 +FRA,-0.0 +GAB,-0.8217118680477142 GBR,-8.699288400748628e-09 GEO,-0.20096751302480698 GHA,-0.5431598126888275 GIN,-0.37204913794994354 -GMB,1.003356397151947 -GNB,0.9321905374526978 +GMB,1.0205875039100647 +GNB,0.9482082426548004 GNQ,-0.7101413011550903 -GRC,-0.150973878800869 -GRD,0.5272164940834045 +GRC,-0.15078746527433395 +GRD,0.5384267568588257 GTM,-2.610952076764761e-08 GUF,-0.9834447503089905 GUY,-1.1032667756080627 HND,-0.6275273263454437 -HRV,-0.13127634674310684 +HRV,-0.12932481989264488 HTI,1.2459450360324809e-08 HUN,-0.4085644632577896 IDN,-0.562181144952774 -IND,1.3695911765098572 +IND,1.3909879922866821 IRL,-0.0 IRN,-0.0 IRQ,-0.0 -ISL,-0.07798495143651962 -ISR,0.5899375379085541 -ITA,0.42469048500061035 -JAM,3.039083242416382 +ISL,-0.07785124331712723 +ISR,0.5995662212371826 +ITA,0.4489997625350952 +JAM,3.0971922874450684 JOR,-0.0 JPN,-0.4728243798017502 -KAZ,-1.748426403658243e-09 +KAZ,-4.997817021346052e-10 KEN,-0.2179667055606842 -KGZ,-7.723922612612455e-10 -KHM,-0.3935643285512924 -KOR,2.681120753288269 -LAO,-0.2158590704202652 -LBN,0.4323943108320236 +KGZ,-1.2954760597594372e-09 +KHM,-0.3926067352294922 +KOR,2.724285364151001 +LAO,-0.21383532136678696 +LBN,0.43942728638648987 LBR,-0.7618840336799622 -LBY,2.1369450653452304e-10 -LKA,-0.39817817509174347 -LSO,-0.48218031227588654 +LBY,3.914085722200866e-10 +LKA,-0.39682164788246155 +LSO,-0.43739476799964905 LTU,-0.0 LUX,0.01513665821403265 LVA,-0.2936539202928543 @@ -101,75 +101,75 @@ MAR,-0.0 MDA,-0.3453979641199112 MDG,-0.6868391931056976 MEX,-0.0679156482219696 -MKD,-0.25515754520893097 +MKD,-0.2545541673898697 MLI,-0.037232923321425915 MLT,-0.0 -MMR,2.3120899200439453 -MNE,-0.21985548734664917 -MNG,-0.029697048477828503 +MMR,2.349255323410034 +MNE,-0.21854685992002487 +MNG,-0.029576445929706097 MOZ,-0.5160333812236786 -MRT,-0.033999696373939514 -MUS,-0.07080470398068428 +MRT,-2.3844572150100163e-10 +MUS,-0.06787611171603203 MWI,-3.3964567824895653e-09 -MYS,-0.2939038872718811 +MYS,-0.29080523550510406 NAM,-0.0047507211565971375 NER,-0.0 NGA,-0.14038769900798798 NIC,-0.5779949724674225 NLD,0.8321638405323029 -NOR,1.1431958079338074 -NPL,0.12020483240485191 +NOR,1.1627331376075745 +NPL,0.12159648910164833 NZL,-0.0 OMN,-0.0 PAK,-0.0 -PAN,-0.6037594079971313 +PAN,-0.21036003530025482 PER,-0.6955994665622711 PHL,-0.11315194144845009 PNG,-0.8056034743785858 POL,0.005302567966282368 -PRI,-0.6564330756664276 +PRI,-2.7517170764213006e-09 PRT,-0.15870346128940582 PRY,-0.6874429285526276 -PSE,0.5565134882926941 +PSE,0.565591037273407 ROU,-0.4107300937175751 -RUS,-2.4574029477975046e-09 +RUS,-3.093318934332956e-09 RWA,1.4384679225543096e-08 SAU,-0.0 -SDN,-0.051845161244273186 -SEN,0.31076647341251373 +SDN,-0.05151940882205963 +SEN,0.3161095827817917 SLB,-1.6454283713329687e-08 SLE,-0.3628302663564682 -SLV,2.9411346912384033 +SLV,2.9967821836471558 SOM,-0.05690718814730644 SRB,-0.2870781123638153 SSD,-0.2865627706050873 -STP,0.49346114695072174 +STP,0.5018362402915955 SUR,-0.13022971898317337 SVK,-0.4101209193468094 -SVN,-0.10804693028330803 -SWE,1.2120238542556763 +SVN,-0.10557281598448753 +SWE,1.232772707939148 SWZ,-2.0683636137164285e-08 -SYR,0.1182437390089035 +SYR,0.12016414105892181 TCD,-0.08657535165548325 TGO,-0.30542606115341187 THA,-1.830725793183774e-08 -TJK,0.3392861485481262 +TJK,0.34667259454727173 TKM,-0.0 TLS,-0.22256753593683243 TTO,-3.4331349985095017e-09 -TUN,0.7436896860599518 -TUR,0.8467051684856415 -TWN,-0.3354679197072983 +TUN,0.7563851475715637 +TUR,0.8610043823719025 +TWN,-0.3345084935426712 TZA,-0.40795499086380005 UGA,-3.941181603295263e-08 -UKR,-0.007475577760487795 +UKR,-1.029895169146755e-09 URY,-0.38374826312065125 -USA,-0.2134283185005188 +USA,-0.21389595419168472 UZB,-0.0 VEN,-0.340562105178833 -VNM,4.7374420166015625 +VNM,4.814816236495972 VUT,-0.19533131271600723 YEM,2.167515278372889e-09 -ZAF,-0.04584670439362526 +ZAF,-5.673877367939895e-09 ZMB,-0.39802390336990356 -ZWE,0.4092240985482931 +ZWE,0.41826839558780193 diff --git a/data/curated/calibration/default/multi_crop_cost.csv b/data/curated/calibration/default/multi_crop_cost.csv index c762260e..a81efde3 100644 --- a/data/curated/calibration/default/multi_crop_cost.csv +++ b/data/curated/calibration/default/multi_crop_cost.csv @@ -1,102 +1,102 @@ combination,country,correction_bnusd_per_mha -cotton_wheat,AFG,-1.5927236080169678 -cotton_wheat,AGO,-1.9337581396102905 +cotton_wheat,AFG,-1.5580675601959229 +cotton_wheat,AGO,-1.8876667022705078 cotton_wheat,ALB,7.5 cotton_wheat,ARG,7.5 cotton_wheat,ARM,7.5 -cotton_wheat,AUS,-6.242731809616089 +cotton_wheat,AUS,-5.869228363037109 cotton_wheat,AZE,7.5 -cotton_wheat,BDI,-0.9871918559074402 -cotton_wheat,BGD,-1.534177005290985 +cotton_wheat,BDI,-0.9778979420661926 +cotton_wheat,BGD,-4.3510295152664185 cotton_wheat,BGR,7.5 cotton_wheat,BOL,7.5 -cotton_wheat,BRA,-2.6939632892608643 -cotton_wheat,BWA,-1.3231643438339233 -cotton_wheat,CHN,-6.612751007080078 -cotton_wheat,COD,-1.9223291873931885 +cotton_wheat,BRA,-2.5362391471862793 +cotton_wheat,BWA,-1.272060751914978 +cotton_wheat,CHN,-6.4746012687683105 +cotton_wheat,COD,-1.9050654172897339 cotton_wheat,DZA,7.5 -cotton_wheat,EGY,-2.767568826675415 -cotton_wheat,ESP,-1.7561184763908386 -cotton_wheat,ETH,-2.117214798927307 +cotton_wheat,EGY,-2.3449623584747314 +cotton_wheat,ESP,-1.2550994753837585 +cotton_wheat,ETH,-2.0870566368103027 cotton_wheat,GEO,7.5 -cotton_wheat,GRC,-2.2709689140319824 -cotton_wheat,HND,-1.9976805448532104 -cotton_wheat,IND,-1.5435030460357666 -cotton_wheat,IRN,-2.3831645250320435 -cotton_wheat,IRQ,-1.8188846111297607 -cotton_wheat,ISR,-0.583361029624939 -cotton_wheat,JOR,-2.195621132850647 -cotton_wheat,KAZ,-2.069269299507141 -cotton_wheat,KEN,-0.7512478828430176 -cotton_wheat,KGZ,-1.4307011365890503 +cotton_wheat,GRC,-2.247580111026764 +cotton_wheat,HND,-1.963274359703064 +cotton_wheat,IND,-1.4972729682922363 +cotton_wheat,IRN,-2.3347407579421997 +cotton_wheat,IRQ,-1.7620608806610107 +cotton_wheat,ISR,-0.538055419921875 +cotton_wheat,JOR,-1.4604971408843994 +cotton_wheat,KAZ,-1.0692015886306763 +cotton_wheat,KEN,-0.7167990803718567 +cotton_wheat,KGZ,-1.3642717599868774 cotton_wheat,KOR,7.5 cotton_wheat,LBN,7.5 -cotton_wheat,LSO,3.307736039161682 -cotton_wheat,MAR,-2.9455323219299316 -cotton_wheat,MDG,-1.7016127109527588 -cotton_wheat,MEX,-3.390718936920166 -cotton_wheat,MKD,-1.731279969215393 +cotton_wheat,LSO,3.3292380273342133 +cotton_wheat,MAR,-1.3864184021949768 +cotton_wheat,MDG,-1.6713669300079346 +cotton_wheat,MEX,-3.298546075820923 +cotton_wheat,MKD,-1.3618669509887695 cotton_wheat,MMR,7.5 -cotton_wheat,MOZ,-1.383345603942871 -cotton_wheat,MWI,2.7875921726226807 -cotton_wheat,NAM,-1.9929966926574707 -cotton_wheat,NER,-0.795464962720871 -cotton_wheat,NGA,-1.8842791318893433 +cotton_wheat,MOZ,-1.3473657369613647 +cotton_wheat,MWI,2.7930944561958313 +cotton_wheat,NAM,-1.947746455669403 +cotton_wheat,NER,-0.7489466965198517 +cotton_wheat,NGA,-1.860558032989502 cotton_wheat,NPL,7.5 cotton_wheat,OMN,-0.0 -cotton_wheat,PAK,-1.3193728923797607 +cotton_wheat,PAK,-1.2731331586837769 cotton_wheat,PRT,7.5 -cotton_wheat,PRY,-0.7623048722743988 -cotton_wheat,PSE,-1.8203025460243225 +cotton_wheat,PRY,-0.7080374360084534 +cotton_wheat,PSE,-1.7600661516189575 cotton_wheat,ROU,-0.0 cotton_wheat,RUS,7.5 -cotton_wheat,RWA,-1.5891630053520203 -cotton_wheat,SDN,-1.7169711589813232 -cotton_wheat,SOM,2.795064091682434 -cotton_wheat,SWZ,-1.7710586786270142 -cotton_wheat,SYR,-1.432285189628601 -cotton_wheat,TJK,-3.2881886959075928 -cotton_wheat,TKM,-1.6561976671218872 +cotton_wheat,RWA,-1.5652288794517517 +cotton_wheat,SDN,-1.6922332048416138 +cotton_wheat,SOM,2.801980435848236 +cotton_wheat,SWZ,-1.7539699077606201 +cotton_wheat,SYR,-1.3698688745498657 +cotton_wheat,TJK,-3.2985422611236572 +cotton_wheat,TKM,-1.6113003492355347 cotton_wheat,TUN,7.5 -cotton_wheat,TUR,-0.9880382418632507 -cotton_wheat,TZA,-1.4977788925170898 -cotton_wheat,UGA,-1.763108491897583 +cotton_wheat,TUR,-0.8975222706794739 +cotton_wheat,TZA,-1.4789782166481018 +cotton_wheat,UGA,-1.734682023525238 cotton_wheat,URY,7.5 -cotton_wheat,USA,-1.4581074714660645 -cotton_wheat,UZB,-1.7531643509864807 -cotton_wheat,YEM,-2.3453104496002197 -cotton_wheat,ZAF,-2.8740453720092773 +cotton_wheat,USA,-1.3568845987319946 +cotton_wheat,UZB,-1.70014888048172 +cotton_wheat,YEM,-1.5217909812927246 +cotton_wheat,ZAF,-2.081026792526245 cotton_wheat,ZMB,7.5 -cotton_wheat,ZWE,3.7642102241516113 +cotton_wheat,ZWE,3.805558681488037 double_rice,AFG,7.5 double_rice,AGO,7.5 double_rice,ALB,-0.0 double_rice,ARG,7.5 double_rice,AUS,7.5 double_rice,AZE,7.5 -double_rice,BDI,3.46013343334198 -double_rice,BEN,-2.2166402339935303 -double_rice,BFA,-2.23768150806427 -double_rice,BGD,-0.7278321385383606 -double_rice,BLZ,2.154790997505188 +double_rice,BDI,3.467510014772415 +double_rice,BEN,-2.8513596057891846 +double_rice,BFA,-2.218084931373596 +double_rice,BGD,-2.353050708770752 +double_rice,BLZ,2.165866494178772 double_rice,BOL,7.5 double_rice,BRA,7.5 -double_rice,BRN,-2.9397683143615723 +double_rice,BRN,-2.9312435388565063 double_rice,BTN,7.5 double_rice,BWA,-0.0 double_rice,CAF,7.5 double_rice,CHL,7.5 -double_rice,CHN,-3.6336244344711304 +double_rice,CHN,-3.5972249507904053 double_rice,CIV,7.5 -double_rice,CMR,-2.1459217071533203 +double_rice,CMR,-2.1260757446289062 double_rice,COD,7.5 double_rice,COG,7.5 -double_rice,COL,-2.7271934747695923 -double_rice,CRI,-1.8571616411209106 +double_rice,COL,-2.7057279348373413 +double_rice,CRI,-1.8212521076202393 double_rice,CUB,7.5 -double_rice,DOM,2.0781034231185913 +double_rice,DOM,2.040722966194153 double_rice,DZA,7.5 -double_rice,ECU,-1.2515701651573181 +double_rice,ECU,-1.2353679537773132 double_rice,EGY,7.5 double_rice,ERI,7.5 double_rice,ESP,7.5 @@ -105,390 +105,390 @@ double_rice,FJI,7.5 double_rice,FRA,7.5 double_rice,GAB,7.5 double_rice,GEO,7.5 -double_rice,GHA,-0.8532624244689941 -double_rice,GIN,-1.1334679126739502 -double_rice,GMB,-0.05453077703714371 -double_rice,GNB,-2.5362197160720825 +double_rice,GHA,-0.8291089832782745 +double_rice,GIN,-7.5 +double_rice,GMB,-0.03783991560339928 +double_rice,GNB,-2.522179961204529 double_rice,GNQ,7.5 double_rice,GRC,7.5 -double_rice,GTM,2.2990827560424805 +double_rice,GTM,2.30462110042572 double_rice,GUF,7.5 double_rice,GUY,7.5 double_rice,HND,7.5 -double_rice,HTI,2.4523184299468994 -double_rice,IDN,-2.7687764167785645 -double_rice,IND,-1.3916572332382202 +double_rice,HTI,2.460844039916992 +double_rice,IDN,-2.8109095096588135 +double_rice,IND,-1.3563387989997864 double_rice,IRN,7.5 double_rice,IRQ,7.5 double_rice,ITA,7.5 -double_rice,JPN,-7.0613017082214355 +double_rice,JPN,-6.999566555023193 double_rice,KEN,7.5 -double_rice,KHM,-2.5964460372924805 +double_rice,KHM,-2.5727648735046387 double_rice,KOR,0.0 -double_rice,LAO,-2.5472428798675537 +double_rice,LAO,-2.522228956222534 double_rice,LBR,7.5 -double_rice,LKA,3.3557786345481873 +double_rice,LKA,3.3700399100780487 double_rice,LSO,-0.0 double_rice,MAR,7.5 -double_rice,MDG,3.56772917509079 +double_rice,MDG,3.5775242745876312 double_rice,MEX,7.5 double_rice,MKD,7.5 -double_rice,MLI,-0.2963952124118805 -double_rice,MMR,-2.7548938989639282 +double_rice,MLI,-0.26757264137268066 +double_rice,MMR,-2.7345409393310547 double_rice,MOZ,7.5 -double_rice,MRT,-1.7550534009933472 +double_rice,MRT,-1.7273759841918945 double_rice,MUS,7.5 -double_rice,MWI,-1.9202125668525696 -double_rice,MYS,-0.9602201282978058 +double_rice,MWI,-1.933668315410614 +double_rice,MYS,-0.9419276714324951 double_rice,NAM,7.5 double_rice,NER,7.5 -double_rice,NGA,-0.4731685668230057 -double_rice,NIC,0.3596424087882042 -double_rice,NPL,3.6091630309820175 +double_rice,NGA,-0.4489987939596176 +double_rice,NIC,3.4304381608963013 +double_rice,NPL,3.6227625012397766 double_rice,PAK,7.5 -double_rice,PAN,-2.171377658843994 +double_rice,PAN,-2.147011876106262 double_rice,PER,7.5 -double_rice,PHL,-1.6855337619781494 +double_rice,PHL,-1.6656925678253174 double_rice,PNG,7.5 double_rice,PRI,7.5 double_rice,PRT,7.5 double_rice,PRY,7.5 -double_rice,RWA,-3.4891295433044434 +double_rice,RWA,-3.4561355113983154 double_rice,SAU,7.5 double_rice,SDN,7.5 -double_rice,SEN,0.19506226480007172 +double_rice,SEN,0.22217439860105515 double_rice,SLB,7.5 -double_rice,SLE,3.5841999650001526 +double_rice,SLE,3.5949014127254486 double_rice,SLV,7.5 double_rice,SOM,7.5 double_rice,SSD,7.5 double_rice,SUR,7.5 double_rice,SWZ,7.5 double_rice,SYR,7.5 -double_rice,TCD,-1.1089786291122437 -double_rice,TGO,-0.26930662244558334 -double_rice,THA,-0.8625509738922119 +double_rice,TCD,-1.094164252281189 +double_rice,TGO,-0.2527042403817177 +double_rice,THA,-0.8441554307937622 double_rice,TJK,7.5 double_rice,TKM,7.5 double_rice,TLS,7.5 double_rice,TTO,7.5 double_rice,TUN,-0.0 double_rice,TUR,7.5 -double_rice,TWN,-1.8933584690093994 +double_rice,TWN,-1.8551788330078125 double_rice,TZA,7.5 double_rice,UGA,7.5 double_rice,URY,7.5 double_rice,USA,7.5 double_rice,UZB,7.5 double_rice,VEN,7.5 -double_rice,VNM,-2.0124760270118713 +double_rice,VNM,-1.9852145910263062 double_rice,YEM,-0.0 double_rice,ZAF,7.5 double_rice,ZMB,7.5 double_rice,ZWE,7.5 -maize_soybean,AFG,-0.3637601435184479 -maize_soybean,AGO,-1.5165324211120605 +maize_soybean,AFG,-0.35400182008743286 +maize_soybean,AGO,-1.5168012976646423 maize_soybean,ALB,7.5 -maize_soybean,ARG,-0.13341142237186432 +maize_soybean,ARG,-0.10373477172106504 maize_soybean,ARM,7.5 -maize_soybean,AUS,1.4551827907562256 -maize_soybean,AUT,4.542241096496582 -maize_soybean,AZE,0.20932435989379883 -maize_soybean,BDI,-1.1280118227005005 +maize_soybean,AUS,1.4967234134674072 +maize_soybean,AUT,4.575681507587433 +maize_soybean,AZE,0.8986120223999023 +maize_soybean,BDI,-1.123425543308258 maize_soybean,BEL,7.5 -maize_soybean,BEN,-0.8633319735527039 -maize_soybean,BFA,-0.6894935369491577 +maize_soybean,BEN,-0.8591903746128082 +maize_soybean,BFA,-0.6823502480983734 maize_soybean,BGD,7.5 maize_soybean,BGR,7.5 maize_soybean,BIH,7.5 -maize_soybean,BLZ,-1.5307040214538574 -maize_soybean,BOL,3.58927221596241 -maize_soybean,BRA,-0.3273313641548157 -maize_soybean,BTN,3.9479890763759613 -maize_soybean,BWA,0.005076378118246794 +maize_soybean,BLZ,-1.5180070400238037 +maize_soybean,BOL,3.5951355397701263 +maize_soybean,BRA,-0.30659323930740356 +maize_soybean,BTN,4.026728093624115 +maize_soybean,BWA,0.0008853892213664949 maize_soybean,CAF,-7.5 -maize_soybean,CAN,-0.6854190826416016 +maize_soybean,CAN,-0.5712883472442627 maize_soybean,CHE,7.5 -maize_soybean,CHN,-1.8604493141174316 -maize_soybean,CIV,-1.049591302871704 -maize_soybean,CMR,-1.1731147766113281 -maize_soybean,COD,-1.6656856536865234 -maize_soybean,COG,-1.5515117645263672 -maize_soybean,COL,-0.5583343803882599 -maize_soybean,CRI,-1.06788170337677 +maize_soybean,CHN,-1.6490706205368042 +maize_soybean,CIV,-1.0419853925704956 +maize_soybean,CMR,-1.1662753224372864 +maize_soybean,COD,-1.6626449823379517 +maize_soybean,COG,-1.5477312207221985 +maize_soybean,COL,-0.5348267257213593 +maize_soybean,CRI,-1.0563188791275024 maize_soybean,CUB,7.5 maize_soybean,CZE,7.5 maize_soybean,DEU,7.5 maize_soybean,DZA,-0.0 -maize_soybean,ECU,-0.8385081887245178 -maize_soybean,EGY,-2.272494316101074 -maize_soybean,ESP,-1.1886811256408691 -maize_soybean,ETH,-0.25956258177757263 +maize_soybean,ECU,-0.8210644721984863 +maize_soybean,EGY,-1.697021484375 +maize_soybean,ESP,-0.6468353271484375 +maize_soybean,ETH,-0.24527738988399506 maize_soybean,FRA,7.5 -maize_soybean,GAB,-1.3096975684165955 -maize_soybean,GEO,-0.011137900874018669 -maize_soybean,GHA,-0.9089171588420868 +maize_soybean,GAB,-1.301273763179779 +maize_soybean,GEO,-0.008261770009994507 +maize_soybean,GHA,-0.9003319442272186 maize_soybean,GIN,7.5 -maize_soybean,GRC,2.8364598155021667 -maize_soybean,GTM,-0.7565690875053406 -maize_soybean,HND,-0.5347414016723633 +maize_soybean,GRC,3.1073732376098633 +maize_soybean,GTM,-0.7456768751144409 +maize_soybean,HND,-0.5240063071250916 maize_soybean,HRV,7.5 -maize_soybean,HUN,0.2438574582338333 -maize_soybean,IDN,0.010410172864794731 -maize_soybean,IND,-0.5793569087982178 -maize_soybean,IRN,-0.6512221097946167 -maize_soybean,IRQ,-0.7892634123563766 +maize_soybean,HUN,-0.3085835129022598 +maize_soybean,IDN,0.05410398915410042 +maize_soybean,IND,-0.56589275598526 +maize_soybean,IRN,-0.6239258646965027 +maize_soybean,IRQ,-0.768975742161274 maize_soybean,ISR,7.5 maize_soybean,ITA,7.5 maize_soybean,JOR,-0.0 maize_soybean,KAZ,7.5 -maize_soybean,KEN,-0.4863159954547882 -maize_soybean,KGZ,-0.46937456727027893 -maize_soybean,KHM,-0.7495225369930267 -maize_soybean,KOR,-0.49779826402664185 -maize_soybean,LAO,-0.23618396371603012 +maize_soybean,KEN,-0.4808295965194702 +maize_soybean,KGZ,-0.4442596137523651 +maize_soybean,KHM,-0.7320345938205719 +maize_soybean,KOR,-0.4821108877658844 +maize_soybean,LAO,-0.21393495798110962 maize_soybean,LBN,7.5 -maize_soybean,LKA,-1.0565711855888367 +maize_soybean,LKA,-1.0435453653335571 maize_soybean,LSO,7.5 maize_soybean,LUX,7.5 maize_soybean,MAR,7.5 maize_soybean,MDA,7.5 -maize_soybean,MEX,-0.09972605854272842 +maize_soybean,MEX,-0.09680690988898277 maize_soybean,MKD,7.5 -maize_soybean,MLI,-0.8281409740447998 -maize_soybean,MMR,-1.1019965410232544 +maize_soybean,MLI,-0.8176049590110779 +maize_soybean,MMR,-1.0901814699172974 maize_soybean,MNE,7.5 -maize_soybean,MOZ,3.766202434897423 -maize_soybean,MWI,3.574208125472069 -maize_soybean,NGA,-0.8216919898986816 -maize_soybean,NIC,-0.42770321667194366 +maize_soybean,MOZ,3.7702455520629883 +maize_soybean,MWI,3.5774897783994675 +maize_soybean,NGA,-0.8138363361358643 +maize_soybean,NIC,0.51187564432621 maize_soybean,NLD,7.5 -maize_soybean,NPL,3.6286289766430855 -maize_soybean,PAK,-0.8721129596233368 +maize_soybean,NPL,3.6355328410863876 +maize_soybean,PAK,-0.8587653934955597 maize_soybean,PER,7.5 maize_soybean,POL,7.5 maize_soybean,PRT,7.5 -maize_soybean,PRY,-0.5639991760253906 -maize_soybean,ROU,3.9618934094905853 +maize_soybean,PRY,-0.5486764311790466 +maize_soybean,ROU,3.864462226629257 maize_soybean,RUS,7.5 -maize_soybean,RWA,-0.39186209440231323 -maize_soybean,SDN,-0.6063498258590698 -maize_soybean,SLV,-0.9797629415988922 +maize_soybean,RWA,-0.3868836313486099 +maize_soybean,SDN,-0.5945940017700195 +maize_soybean,SLV,-0.9693711698055267 maize_soybean,SRB,7.5 maize_soybean,SSD,7.5 maize_soybean,SVK,7.5 maize_soybean,SVN,7.5 -maize_soybean,SWZ,-0.899118959903717 +maize_soybean,SWZ,-0.9007896184921265 maize_soybean,SYR,7.5 -maize_soybean,TCD,3.510473385453224 -maize_soybean,TGO,-0.4266141951084137 -maize_soybean,THA,-0.5974937677383423 +maize_soybean,TCD,3.51370108127594 +maize_soybean,TGO,-0.4224013090133667 +maize_soybean,THA,-0.5829336643218994 maize_soybean,TJK,7.5 maize_soybean,TKM,7.5 -maize_soybean,TLS,4.0747289955616 +maize_soybean,TLS,4.086310833692551 maize_soybean,TUR,7.5 maize_soybean,TWN,-0.0 -maize_soybean,TZA,-0.4159565716981888 -maize_soybean,UGA,-1.4297746419906616 +maize_soybean,TZA,-0.41082581877708435 +maize_soybean,UGA,-1.4239206314086914 maize_soybean,UKR,7.5 -maize_soybean,URY,-0.20672881603240967 -maize_soybean,USA,-0.8845836520195007 +maize_soybean,URY,-0.18123215436935425 +maize_soybean,USA,-0.7721644043922424 maize_soybean,UZB,7.5 -maize_soybean,VEN,-0.9502752423286438 -maize_soybean,VNM,-0.4805498272180557 -maize_soybean,ZAF,-0.31235234439373016 -maize_soybean,ZMB,-0.6437894999980927 -maize_soybean,ZWE,0.8828884959220886 -rice_maize,AFG,-1.7254143953323364 -rice_maize,AGO,-2.3706471920013428 +maize_soybean,VEN,-0.93727245926857 +maize_soybean,VNM,-0.45424652099609375 +maize_soybean,ZAF,0.07234642095863819 +maize_soybean,ZMB,-0.6451443433761597 +maize_soybean,ZWE,0.8661841154098511 +rice_maize,AFG,-1.7071537971496582 +rice_maize,AGO,-2.3692575693130493 rice_maize,ALB,-0.0 rice_maize,ARG,7.5 rice_maize,ARM,-0.0 -rice_maize,AUS,-2.181629180908203 -rice_maize,AZE,-3.527411937713623 -rice_maize,BDI,-0.7213936448097229 -rice_maize,BEN,-1.6537254452705383 -rice_maize,BGD,0.6719281077384949 +rice_maize,AUS,-1.6404647827148438 +rice_maize,AZE,-2.7403345108032227 +rice_maize,BDI,-0.710191011428833 +rice_maize,BEN,-1.6371826529502869 +rice_maize,BGD,-7.025854587554932 rice_maize,BGR,7.5 -rice_maize,BLZ,-2.5882227420806885 +rice_maize,BLZ,-2.566882610321045 rice_maize,BOL,7.5 -rice_maize,BRA,-0.18532046675682068 -rice_maize,BTN,-0.7807840406894684 +rice_maize,BRA,-0.13622650504112244 +rice_maize,BTN,-0.4435684159398079 rice_maize,BWA,-0.0 -rice_maize,CAF,-2.386669635772705 +rice_maize,CAF,-2.380967617034912 rice_maize,CAN,-0.0 -rice_maize,CHL,-1.781331181526184 -rice_maize,CHN,-3.118068218231201 -rice_maize,CIV,-0.9498778581619263 +rice_maize,CHL,-1.7334457635879517 +rice_maize,CHN,-3.0255298614501953 +rice_maize,CIV,-0.9353621602058411 rice_maize,CMR,7.5 -rice_maize,COD,-2.5151937007904053 -rice_maize,COL,-1.9840177297592163 -rice_maize,COM,-2.3835904598236084 +rice_maize,COD,-2.509294629096985 +rice_maize,COL,-1.9580504298210144 +rice_maize,COM,-2.385923147201538 rice_maize,CUB,7.5 -rice_maize,DOM,2.363918662071228 +rice_maize,DOM,2.321830630302429 rice_maize,DZA,7.5 -rice_maize,ECU,-0.6062188744544983 -rice_maize,EGY,-2.7201571464538574 +rice_maize,ECU,-0.5714932680130005 +rice_maize,EGY,-2.4941112995147705 rice_maize,ERI,7.5 -rice_maize,ESP,-3.4109416007995605 +rice_maize,ESP,-1.9187513589859009 rice_maize,ETH,7.5 rice_maize,FRA,7.5 rice_maize,GEO,7.5 -rice_maize,GHA,-1.0155074894428253 -rice_maize,GIN,-0.684197187423706 -rice_maize,GNB,-1.6221559047698975 -rice_maize,GRC,-2.7906360626220703 -rice_maize,GTM,-2.216136932373047 -rice_maize,HND,-1.7721517086029053 -rice_maize,HTI,-2.207491397857666 +rice_maize,GHA,-0.9996322691440582 +rice_maize,GIN,-0.9031546413898468 +rice_maize,GNB,-1.6037192344665527 +rice_maize,GRC,-2.1628137826919556 +rice_maize,GTM,-2.20433509349823 +rice_maize,HND,-1.7619343996047974 +rice_maize,HTI,-2.196060061454773 rice_maize,HUN,7.5 rice_maize,IDN,-0.0 -rice_maize,IND,-1.1106226444244385 -rice_maize,IRN,-4.246026992797852 -rice_maize,IRQ,-2.36727511882782 -rice_maize,ITA,-3.0518001317977905 -rice_maize,KAZ,-0.9933299422264099 -rice_maize,KEN,-1.2865784764289856 -rice_maize,KGZ,-3.3919849395751953 -rice_maize,KOR,-6.837083578109741 -rice_maize,LAO,-1.5965595245361328 -rice_maize,LKA,-1.2170969247817993 +rice_maize,IND,-1.0832955837249756 +rice_maize,IRN,-4.213629484176636 +rice_maize,IRQ,-2.343656897544861 +rice_maize,ITA,-4.836171627044678 +rice_maize,KAZ,0.12270903214812279 +rice_maize,KEN,-1.5401140451431274 +rice_maize,KGZ,-3.360135078430176 +rice_maize,KOR,-6.816782712936401 +rice_maize,LAO,-1.5692051649093628 +rice_maize,LKA,-1.1975427865982056 rice_maize,LSO,-0.0 -rice_maize,MAR,2.6333963871002197 +rice_maize,MAR,1.120978832244873 rice_maize,MDA,7.5 -rice_maize,MDG,-1.2474266290664673 -rice_maize,MEX,-0.519189178943634 +rice_maize,MDG,-1.2463083267211914 +rice_maize,MEX,-0.4782504588365555 rice_maize,MKD,7.5 -rice_maize,MLI,-0.5910901427268982 -rice_maize,MMR,-1.850481390953064 -rice_maize,MOZ,-0.5015707612037659 +rice_maize,MLI,-0.5725135207176208 +rice_maize,MMR,-1.827895998954773 +rice_maize,MOZ,-0.49565383791923523 rice_maize,MUS,7.5 -rice_maize,MWI,-1.2465201616287231 +rice_maize,MWI,-1.2369296550750732 rice_maize,NAM,7.5 -rice_maize,NGA,-0.7111078500747681 -rice_maize,NIC,-0.0 -rice_maize,NPL,-0.5173944532871246 -rice_maize,PAK,-1.424728274345398 -rice_maize,PER,-2.36906361579895 -rice_maize,PHL,-1.1037707924842834 +rice_maize,NGA,-0.6966127157211304 +rice_maize,NIC,0.6219503879547119 +rice_maize,NPL,-0.4983917698264122 +rice_maize,PAK,-1.397399663925171 +rice_maize,PER,-1.4915390014648438 +rice_maize,PHL,-1.081274390220642 rice_maize,PRI,7.5 -rice_maize,PRT,2.988905191421509 -rice_maize,PRY,-1.077894926071167 -rice_maize,ROU,-1.5070655941963196 +rice_maize,PRT,3.01252019405365 +rice_maize,PRY,-1.0557880401611328 +rice_maize,ROU,-1.8331242799758911 rice_maize,RUS,7.5 -rice_maize,RWA,-2.056783676147461 +rice_maize,RWA,-2.0356147289276123 rice_maize,SAU,7.5 -rice_maize,SDN,-2.5225976705551147 +rice_maize,SDN,-2.5177301168441772 rice_maize,SEN,7.5 rice_maize,SLE,7.5 -rice_maize,SLV,-2.8895020484924316 -rice_maize,SOM,-0.4468875229358673 -rice_maize,SSD,-2.357115387916565 +rice_maize,SLV,-2.807238221168518 +rice_maize,SOM,-0.4101323187351227 +rice_maize,SSD,-2.3532315492630005 rice_maize,SVK,-0.0 rice_maize,SVN,-0.0 rice_maize,SWZ,7.5 -rice_maize,SYR,-0.23338383436203003 -rice_maize,TCD,-0.5543370246887207 -rice_maize,TGO,-0.4559953808784485 -rice_maize,THA,-0.9153203964233398 -rice_maize,TJK,-3.795152425765991 -rice_maize,TKM,-1.270441710948944 -rice_maize,TLS,-1.3026959896087646 +rice_maize,SYR,-0.19503404200077057 +rice_maize,TCD,-0.5403998494148254 +rice_maize,TGO,-0.4454067051410675 +rice_maize,THA,-0.8962728977203369 +rice_maize,TJK,-3.7927963733673096 +rice_maize,TKM,-1.2461795806884766 +rice_maize,TLS,-1.2771695852279663 rice_maize,TUN,-0.0 -rice_maize,TUR,-2.4413100481033325 -rice_maize,TZA,-0.05638634040951729 -rice_maize,UGA,-2.277077317237854 +rice_maize,TUR,-2.4231834411621094 +rice_maize,TZA,-0.03729932755231857 +rice_maize,UGA,-2.2692826986312866 rice_maize,UKR,7.5 -rice_maize,URY,-0.6884299516677856 -rice_maize,USA,-1.7078514099121094 -rice_maize,UZB,-5.904582977294922 -rice_maize,VEN,-1.1892468333244324 -rice_maize,VNM,-1.2525776624679565 +rice_maize,URY,-0.6412667632102966 +rice_maize,USA,-1.6199488639831543 +rice_maize,UZB,-5.8712663650512695 +rice_maize,VEN,-1.1588536500930786 +rice_maize,VNM,-1.218591332435608 rice_maize,YEM,-0.0 -rice_maize,ZAF,-1.193888247013092 -rice_maize,ZMB,-1.0817012786865234 +rice_maize,ZAF,-0.6643975377082825 +rice_maize,ZMB,-1.0811877250671387 rice_maize,ZWE,7.5 -rice_wheat,AFG,-1.3595799207687378 +rice_wheat,AFG,-1.3394337892532349 rice_wheat,AGO,7.5 rice_wheat,ALB,-0.0 rice_wheat,ARG,7.5 rice_wheat,ARM,-0.0 -rice_wheat,AUS,-2.0604162216186523 -rice_wheat,AZE,-3.140916109085083 -rice_wheat,BGD,-0.693154513835907 -rice_wheat,BGR,2.8220043778419495 +rice_wheat,AUS,-1.5956697463989258 +rice_wheat,AZE,-2.685837984085083 +rice_wheat,BGD,-4.662807941436768 +rice_wheat,BGR,2.5431015491485596 rice_wheat,BOL,7.5 rice_wheat,BRA,7.5 -rice_wheat,BTN,-0.4830802381038666 +rice_wheat,BTN,-0.17724381014704704 rice_wheat,BWA,-0.0 rice_wheat,CAN,-0.0 -rice_wheat,CHL,-1.281676173210144 -rice_wheat,CHN,-3.024598717689514 -rice_wheat,CMR,-1.7146903276443481 -rice_wheat,COD,-2.2412753105163574 +rice_wheat,CHL,-1.2441322207450867 +rice_wheat,CHN,-2.962647318840027 +rice_wheat,CMR,-1.7017266750335693 +rice_wheat,COD,-2.234706401824951 rice_wheat,DZA,7.5 -rice_wheat,ECU,-1.3063002824783325 -rice_wheat,EGY,-2.394174098968506 -rice_wheat,ESP,-2.19299054145813 +rice_wheat,ECU,-1.2989791631698608 +rice_wheat,EGY,-2.175260543823242 +rice_wheat,ESP,-1.2148844003677368 rice_wheat,ETH,7.5 rice_wheat,FRA,7.5 rice_wheat,GEO,7.5 -rice_wheat,GRC,-1.4912328124046326 +rice_wheat,GRC,-1.1102929413318634 rice_wheat,GTM,7.5 -rice_wheat,HND,-2.0529308319091797 +rice_wheat,HND,-2.042919874191284 rice_wheat,HUN,7.5 -rice_wheat,IND,-1.0325279235839844 -rice_wheat,IRN,-3.5437495708465576 -rice_wheat,IRQ,-2.0931166410446167 -rice_wheat,ITA,-2.4195598363876343 -rice_wheat,JPN,-0.9091683030128479 -rice_wheat,KAZ,-1.054283857345581 -rice_wheat,KGZ,-3.27589750289917 -rice_wheat,KOR,-6.689305782318115 +rice_wheat,IND,-1.0064887404441833 +rice_wheat,IRN,-3.5207117795944214 +rice_wheat,IRQ,-2.075403332710266 +rice_wheat,ITA,-3.4701508283615112 +rice_wheat,JPN,-0.8453636765480042 +rice_wheat,KAZ,-0.3687819242477417 +rice_wheat,KGZ,-3.2588419914245605 +rice_wheat,KOR,-6.670114040374756 rice_wheat,LAO,-0.0 rice_wheat,LSO,-0.0 -rice_wheat,MAR,-2.4089279174804688 +rice_wheat,MAR,-3.1273651123046875 rice_wheat,MDA,7.5 -rice_wheat,MDG,-0.9571394920349121 +rice_wheat,MDG,-0.9462524652481079 rice_wheat,MEX,7.5 rice_wheat,MKD,7.5 -rice_wheat,MLI,-0.5888196229934692 -rice_wheat,MMR,-1.8358181715011597 +rice_wheat,MLI,-0.5666029453277588 +rice_wheat,MMR,-1.817818522453308 rice_wheat,MOZ,7.5 rice_wheat,MRT,7.5 -rice_wheat,MWI,2.8601826429367065 -rice_wheat,NAM,2.692684054374695 -rice_wheat,NER,-1.397388756275177 -rice_wheat,NGA,-0.8274063467979431 -rice_wheat,NPL,-0.7158598005771637 -rice_wheat,PAK,-1.44150972366333 -rice_wheat,PRT,-1.3273289203643799 -rice_wheat,PRY,-0.9229036867618561 +rice_wheat,MWI,2.86371773481369 +rice_wheat,NAM,2.7019991874694824 +rice_wheat,NER,-1.3739266991615295 +rice_wheat,NGA,-0.8106486797332764 +rice_wheat,NPL,-0.6994288563728333 +rice_wheat,PAK,-1.422066569328308 +rice_wheat,PRT,-1.301887035369873 +rice_wheat,PRY,-0.90675288438797 rice_wheat,ROU,7.5 rice_wheat,RUS,7.5 -rice_wheat,RWA,-2.214703321456909 -rice_wheat,SDN,-1.564651370048523 +rice_wheat,RWA,-2.1962409019470215 +rice_wheat,SDN,-1.5904009342193604 rice_wheat,SOM,7.5 rice_wheat,SVK,-0.0 rice_wheat,SVN,-0.0 rice_wheat,SWZ,7.5 -rice_wheat,SYR,-0.490796834230423 -rice_wheat,TCD,-0.8735635578632355 +rice_wheat,SYR,-0.46226635575294495 +rice_wheat,TCD,-0.8584619760513306 rice_wheat,THA,7.5 -rice_wheat,TJK,-3.7066290378570557 -rice_wheat,TKM,-1.3613498210906982 +rice_wheat,TJK,-3.7356550693511963 +rice_wheat,TKM,-1.3425502181053162 rice_wheat,TUN,-0.0 -rice_wheat,TUR,-2.2071304321289062 -rice_wheat,TZA,-0.48743367195129395 -rice_wheat,UGA,-2.0336015224456787 +rice_wheat,TUR,-2.2004952430725098 +rice_wheat,TZA,-0.4745228588581085 +rice_wheat,UGA,-2.0261285305023193 rice_wheat,UKR,7.5 -rice_wheat,URY,-0.607020765542984 -rice_wheat,USA,-1.4283429980278015 -rice_wheat,UZB,-4.8279805183410645 +rice_wheat,URY,-0.5700052976608276 +rice_wheat,USA,-1.3874574899673462 +rice_wheat,UZB,-4.804621696472168 rice_wheat,VEN,7.5 -rice_wheat,VNM,-1.4757278561592102 -rice_wheat,ZAF,-1.3662726879119873 -rice_wheat,ZMB,-0.8025999069213867 +rice_wheat,VNM,-1.4548847079277039 +rice_wheat,ZAF,-1.0357567071914673 +rice_wheat,ZMB,-0.7816215753555298 rice_wheat,ZWE,7.5 triple_rice,AFG,7.5 triple_rice,AGO,7.5 @@ -497,12 +497,12 @@ triple_rice,AUS,7.5 triple_rice,BDI,7.5 triple_rice,BEN,7.5 triple_rice,BFA,7.5 -triple_rice,BGD,-0.4842730015516281 +triple_rice,BGD,-7.5 triple_rice,BLZ,7.5 triple_rice,BOL,7.5 triple_rice,BRA,7.5 triple_rice,BRN,7.5 -triple_rice,BTN,-3.1066852807998657 +triple_rice,BTN,-2.444580078125 triple_rice,BWA,-0.0 triple_rice,CHN,7.5 triple_rice,CIV,7.5 @@ -527,7 +527,7 @@ triple_rice,GMB,7.5 triple_rice,GNB,7.5 triple_rice,GNQ,7.5 triple_rice,GRC,7.5 -triple_rice,GTM,1.568363904953003 +triple_rice,GTM,1.5765612125396729 triple_rice,GUF,7.5 triple_rice,GUY,7.5 triple_rice,HND,7.5 @@ -538,7 +538,7 @@ triple_rice,IRN,7.5 triple_rice,IRQ,7.5 triple_rice,ITA,7.5 triple_rice,KEN,7.5 -triple_rice,KHM,-3.8946691751480103 +triple_rice,KHM,-3.8591474294662476 triple_rice,LAO,7.5 triple_rice,LBR,7.5 triple_rice,LKA,7.5 @@ -568,7 +568,7 @@ triple_rice,RWA,7.5 triple_rice,SAU,7.5 triple_rice,SDN,7.5 triple_rice,SEN,7.5 -triple_rice,SLB,-4.461336374282837 +triple_rice,SLB,-4.446496486663818 triple_rice,SLE,7.5 triple_rice,SLV,7.5 triple_rice,SOM,7.5 @@ -578,7 +578,7 @@ triple_rice,SWZ,7.5 triple_rice,TCD,7.5 triple_rice,TGO,7.5 triple_rice,THA,7.5 -triple_rice,TLS,1.2680587768554688 +triple_rice,TLS,1.2825658321380615 triple_rice,TTO,7.5 triple_rice,TUR,7.5 triple_rice,TWN,7.5 @@ -587,204 +587,204 @@ triple_rice,UGA,7.5 triple_rice,URY,7.5 triple_rice,USA,7.5 triple_rice,VEN,7.5 -triple_rice,VNM,2.5807573795318604 +triple_rice,VNM,2.609875798225403 triple_rice,YEM,-0.0 triple_rice,ZAF,7.5 triple_rice,ZMB,7.5 triple_rice,ZWE,7.5 -wheat_maize,AFG,-0.9849343299865723 -wheat_maize,AGO,-1.5589878559112549 +wheat_maize,AFG,-0.970360279083252 +wheat_maize,AGO,-1.5584815740585327 wheat_maize,ALB,7.5 wheat_maize,ARE,7.5 wheat_maize,ARG,7.5 -wheat_maize,ARM,-0.20182248950004578 -wheat_maize,AUS,0.22560419142246246 +wheat_maize,ARM,-0.1766364872455597 +wheat_maize,AUS,0.26962894201278687 wheat_maize,AUT,7.5 -wheat_maize,AZE,-0.6330894827842712 -wheat_maize,BDI,-1.127558708190918 +wheat_maize,AZE,-0.12756909430027008 +wheat_maize,BDI,-1.121222972869873 wheat_maize,BEL,7.5 -wheat_maize,BGD,0.10837168991565704 +wheat_maize,BGD,-2.9793293178081512 wheat_maize,BGR,7.5 wheat_maize,BIH,7.5 -wheat_maize,BOL,-0.5363734364509583 -wheat_maize,BRA,-0.18438968807458878 -wheat_maize,BTN,0.9781211018562317 -wheat_maize,BWA,-0.10034539550542831 +wheat_maize,BOL,-0.5250248312950134 +wheat_maize,BRA,-0.1688058152794838 +wheat_maize,BTN,1.2078960537910461 +wheat_maize,BWA,-0.09905508905649185 wheat_maize,CAN,7.5 wheat_maize,CHE,7.5 -wheat_maize,CHL,-1.4138022661209106 -wheat_maize,CHN,-2.4889100790023804 -wheat_maize,CMR,-0.8750249147415161 -wheat_maize,COD,-1.5499111413955688 -wheat_maize,COL,-1.0134680271148682 +wheat_maize,CHL,-1.3656971454620361 +wheat_maize,CHN,-2.4295815229415894 +wheat_maize,CMR,-0.8586012423038483 +wheat_maize,COD,-1.543189525604248 +wheat_maize,COL,-0.9841573238372803 wheat_maize,CZE,7.5 wheat_maize,DEU,7.5 wheat_maize,DZA,7.5 -wheat_maize,ECU,-1.1721757650375366 -wheat_maize,EGY,-2.27541184425354 -wheat_maize,ERI,-1.4826164841651917 -wheat_maize,ESP,-0.361922062933445 -wheat_maize,ETH,-0.4375545382499695 +wheat_maize,ECU,-1.1564401388168335 +wheat_maize,EGY,-1.9909616708755493 +wheat_maize,ERI,-1.4761410355567932 +wheat_maize,ESP,-0.22721823304891586 +wheat_maize,ETH,-0.4192868173122406 wheat_maize,FRA,7.5 -wheat_maize,GEO,0.03534886613488197 -wheat_maize,GRC,2.8829175233840942 -wheat_maize,GTM,3.285066694021225 -wheat_maize,HND,-1.1586399674415588 +wheat_maize,GEO,0.04760634899139404 +wheat_maize,GRC,3.1234430074691772 +wheat_maize,GTM,3.2949488759040833 +wheat_maize,HND,-1.149836540222168 wheat_maize,HRV,7.5 -wheat_maize,HUN,-0.1818016991019249 -wheat_maize,IND,-0.5853933691978455 -wheat_maize,IRN,-1.325901746749878 -wheat_maize,IRQ,-1.3061878681182861 -wheat_maize,ISR,-6.799508094787598 -wheat_maize,ITA,3.2091699838638306 -wheat_maize,JOR,-0.4680543839931488 -wheat_maize,KAZ,-0.6769985556602478 -wheat_maize,KEN,-0.8018709123134613 -wheat_maize,KGZ,-0.4710511565208435 -wheat_maize,KOR,-0.32269126176834106 +wheat_maize,HUN,-0.5560410916805267 +wheat_maize,IND,-0.5659535527229309 +wheat_maize,IRN,-1.2991694808006287 +wheat_maize,IRQ,-1.2828751802444458 +wheat_maize,ISR,-6.803310394287109 +wheat_maize,ITA,2.7400755882263184 +wheat_maize,JOR,-0.008941784501075745 +wheat_maize,KAZ,0.08618625998497009 +wheat_maize,KEN,-0.7920613288879395 +wheat_maize,KGZ,-0.4411478638648987 +wheat_maize,KOR,-0.2993202209472656 wheat_maize,LAO,-0.0 -wheat_maize,LBN,-0.6227757036685944 +wheat_maize,LBN,-0.6043627262115479 wheat_maize,LBY,7.5 -wheat_maize,LSO,-0.015198908746242523 +wheat_maize,LSO,-0.0154474638402462 wheat_maize,LUX,7.5 -wheat_maize,MAR,-0.5118548274040222 +wheat_maize,MAR,-2.2815093994140625 wheat_maize,MDA,7.5 -wheat_maize,MDG,-1.1886930465698242 -wheat_maize,MEX,-0.8947439193725586 +wheat_maize,MDG,-1.1845978498458862 +wheat_maize,MEX,-0.9270248711109161 wheat_maize,MKD,7.5 -wheat_maize,MMR,-0.5519289374351501 +wheat_maize,MMR,-0.5239141583442688 wheat_maize,MNE,7.5 -wheat_maize,MOZ,-0.27184000611305237 -wheat_maize,MWI,3.3756099939346313 -wheat_maize,NAM,-1.0349177122116089 +wheat_maize,MOZ,-0.2600785866379738 +wheat_maize,MWI,3.3794741928577423 +wheat_maize,NAM,-1.0257103741168976 wheat_maize,NIC,7.5 wheat_maize,NLD,7.5 -wheat_maize,NPL,-0.5624594837427139 -wheat_maize,NZL,-1.7070268988609314 -wheat_maize,OMN,-3.4702497720718384 -wheat_maize,PAK,-0.6241451501846313 -wheat_maize,PER,-1.0873700976371765 +wheat_maize,NPL,-0.546818733215332 +wheat_maize,NZL,-1.6498069167137146 +wheat_maize,OMN,-4.052239418029785 +wheat_maize,PAK,-0.6015169620513916 +wheat_maize,PER,-0.4222402125597 wheat_maize,POL,7.5 -wheat_maize,PRT,-0.3758561760187149 -wheat_maize,PRY,-0.42313165962696075 -wheat_maize,PSE,1.4735210537910461 +wheat_maize,PRT,-0.3346504122018814 +wheat_maize,PRY,-0.4048227220773697 +wheat_maize,PSE,1.5346109867095947 wheat_maize,ROU,7.5 wheat_maize,RUS,7.5 -wheat_maize,RWA,-0.5481820106506348 -wheat_maize,SAU,-4.958998918533325 +wheat_maize,RWA,-0.5387715697288513 +wheat_maize,SAU,-4.849274396896362 wheat_maize,SLV,-0.0 -wheat_maize,SOM,0.08026691898703575 +wheat_maize,SOM,0.10931055620312691 wheat_maize,SRB,7.5 wheat_maize,SSD,7.5 wheat_maize,SVK,7.5 wheat_maize,SVN,7.5 -wheat_maize,SWZ,-1.235953450202942 -wheat_maize,SYR,-0.3142406642436981 +wheat_maize,SWZ,-1.2312324047088623 +wheat_maize,SYR,-0.2919238433241844 wheat_maize,THA,7.5 -wheat_maize,TJK,-0.6639165282249451 -wheat_maize,TKM,-0.8205778002738953 +wheat_maize,TJK,-0.6208418011665344 +wheat_maize,TKM,-0.8047029972076416 wheat_maize,TUN,-0.0 -wheat_maize,TUR,0.37567462027072906 -wheat_maize,TWN,-0.008953025564551353 -wheat_maize,TZA,-0.3282812088727951 -wheat_maize,UGA,-1.2599499225616455 +wheat_maize,TUR,0.38710957765579224 +wheat_maize,TWN,0.02839224599301815 +wheat_maize,TZA,-0.32132773101329803 +wheat_maize,UGA,-1.2502294182777405 wheat_maize,UKR,7.5 -wheat_maize,URY,-0.4269259050488472 -wheat_maize,USA,-0.7423370778560638 -wheat_maize,UZB,-1.9013787508010864 +wheat_maize,URY,-0.3946928009390831 +wheat_maize,USA,-0.6116225719451904 +wheat_maize,UZB,-1.8715513944625854 wheat_maize,VEN,7.5 -wheat_maize,VNM,-0.46025991439819336 -wheat_maize,YEM,-1.6670135855674744 -wheat_maize,ZAF,-0.3842948079109192 -wheat_maize,ZMB,0.3949296474456787 +wheat_maize,VNM,-0.42623478174209595 +wheat_maize,YEM,-0.883408397436142 +wheat_maize,ZAF,-0.19190656393766403 +wheat_maize,ZMB,0.40779826045036316 wheat_maize,ZWE,7.5 -wheat_soybean,AFG,0.023451562970876694 -wheat_soybean,AGO,-1.297289252281189 +wheat_soybean,AFG,0.03661375865340233 +wheat_soybean,AGO,-1.2946367263793945 wheat_soybean,ALB,7.5 wheat_soybean,ARG,7.5 wheat_soybean,ARM,7.5 -wheat_soybean,AUS,0.8355346322059631 -wheat_soybean,AUT,4.0272236764431 -wheat_soybean,AZE,0.6157877445220947 -wheat_soybean,BDI,-1.5064332485198975 +wheat_soybean,AUS,0.8510012030601501 +wheat_soybean,AUT,4.040925741195679 +wheat_soybean,AZE,0.9479256868362427 +wheat_soybean,BDI,-1.5036457777023315 wheat_soybean,BEL,7.5 -wheat_soybean,BGD,3.5075069069862366 +wheat_soybean,BGD,3.6643032133579254 wheat_soybean,BGR,7.5 wheat_soybean,BIH,7.5 wheat_soybean,BOL,7.5 -wheat_soybean,BRA,-0.31569650769233704 +wheat_soybean,BRA,-0.3326617628335953 wheat_soybean,BTN,7.5 wheat_soybean,BWA,7.5 wheat_soybean,CAN,7.5 wheat_soybean,CHE,7.5 -wheat_soybean,CHN,-1.6905781626701355 -wheat_soybean,CMR,-0.6206047534942627 -wheat_soybean,COD,-1.3283783197402954 +wheat_soybean,CHN,-1.506752371788025 +wheat_soybean,CMR,-0.6081311106681824 +wheat_soybean,COD,-1.3231159448623657 wheat_soybean,CZE,7.5 wheat_soybean,DEU,7.5 wheat_soybean,DZA,-0.0 -wheat_soybean,ECU,-0.7177078425884247 -wheat_soybean,EGY,-1.7534197568893433 -wheat_soybean,ESP,-0.0031415356788784266 -wheat_soybean,ETH,-0.9896889925003052 +wheat_soybean,ECU,-0.7124471962451935 +wheat_soybean,EGY,-1.5192029476165771 +wheat_soybean,ESP,0.0497831255197525 +wheat_soybean,ETH,-0.9822540283203125 wheat_soybean,FRA,7.5 -wheat_soybean,GEO,-0.012631510384380817 -wheat_soybean,GRC,3.4658419489860535 -wheat_soybean,GTM,3.733000973239541 -wheat_soybean,HND,-0.9046487808227539 +wheat_soybean,GEO,-0.01244677510112524 +wheat_soybean,GRC,3.5737907737493515 +wheat_soybean,GTM,3.7417505653575063 +wheat_soybean,HND,-0.97734135389328 wheat_soybean,HRV,7.5 -wheat_soybean,HUN,0.07651042938232422 -wheat_soybean,IND,-0.45092836022377014 -wheat_soybean,IRN,-0.3359500914812088 -wheat_soybean,IRQ,-0.634438656270504 +wheat_soybean,HUN,-0.35626834630966187 +wheat_soybean,IND,-0.4400443434715271 +wheat_soybean,IRN,-0.3219611495733261 +wheat_soybean,IRQ,-0.6229656338691711 wheat_soybean,ISR,7.5 wheat_soybean,ITA,7.5 wheat_soybean,JOR,-0.0 -wheat_soybean,JPN,1.758805274963379 +wheat_soybean,JPN,1.7895840406417847 wheat_soybean,KAZ,7.5 -wheat_soybean,KEN,-0.7852859199047089 -wheat_soybean,KGZ,-0.38569173216819763 -wheat_soybean,KOR,3.4483194947242737 +wheat_soybean,KEN,-0.7790870070457458 +wheat_soybean,KGZ,-0.37603795528411865 +wheat_soybean,KOR,3.4533695578575134 wheat_soybean,LAO,-0.0 wheat_soybean,LBN,7.5 wheat_soybean,LSO,7.5 wheat_soybean,LUX,7.5 -wheat_soybean,MAR,-1.7598727345466614 +wheat_soybean,MAR,-1.4468068480491638 wheat_soybean,MDA,7.5 -wheat_soybean,MEX,-0.4874807149171829 +wheat_soybean,MEX,-0.4815727025270462 wheat_soybean,MKD,7.5 -wheat_soybean,MMR,3.7208532635122538 +wheat_soybean,MMR,3.7270175460726023 wheat_soybean,MNE,7.5 wheat_soybean,MOZ,7.5 -wheat_soybean,MWI,3.2981008887290955 +wheat_soybean,MWI,3.299912750720978 wheat_soybean,NGA,7.5 wheat_soybean,NLD,7.5 wheat_soybean,NPL,7.5 -wheat_soybean,PAK,-0.6318481862545013 -wheat_soybean,PER,-0.573964536190033 +wheat_soybean,PAK,-0.6219449937343597 +wheat_soybean,PER,0.06351040303707123 wheat_soybean,POL,7.5 wheat_soybean,PRT,7.5 -wheat_soybean,PRY,-0.40803292393684387 +wheat_soybean,PRY,-0.4000007212162018 wheat_soybean,ROU,7.5 wheat_soybean,RUS,7.5 -wheat_soybean,RWA,-0.5008382648229599 +wheat_soybean,RWA,-0.4971163123846054 wheat_soybean,SRB,7.5 wheat_soybean,SVK,7.5 wheat_soybean,SVN,7.5 -wheat_soybean,SWZ,-0.6631672978401184 +wheat_soybean,SWZ,-0.6537529826164246 wheat_soybean,SYR,7.5 wheat_soybean,THA,7.5 wheat_soybean,TJK,7.5 -wheat_soybean,TKM,4.054523408412933 +wheat_soybean,TKM,4.06177294254303 wheat_soybean,TUR,7.5 -wheat_soybean,TWN,-0.5627845525741577 -wheat_soybean,TZA,-0.5059201717376709 -wheat_soybean,UGA,-1.2213156819343567 +wheat_soybean,TWN,-0.5503105521202087 +wheat_soybean,TZA,-0.5011659264564514 +wheat_soybean,UGA,-1.215870976448059 wheat_soybean,UKR,7.5 -wheat_soybean,URY,-0.11813099682331085 -wheat_soybean,USA,-0.30147039890289307 +wheat_soybean,URY,-0.1030755490064621 +wheat_soybean,USA,-0.26650673151016235 wheat_soybean,UZB,7.5 -wheat_soybean,VNM,-0.8296979069709778 -wheat_soybean,ZAF,-0.5150110721588135 -wheat_soybean,ZMB,-0.4146267771720886 -wheat_soybean,ZWE,4.302780628204346 +wheat_soybean,VNM,-0.816567599773407 +wheat_soybean,ZAF,-0.3267609477043152 +wheat_soybean,ZMB,-0.39901798963546753 +wheat_soybean,ZWE,4.313300132751465 diff --git a/data/curated/calibration/default/provenance.yaml b/data/curated/calibration/default/provenance.yaml index 8991605c..07b62ae9 100644 --- a/data/curated/calibration/default/provenance.yaml +++ b/data/curated/calibration/default/provenance.yaml @@ -4,8 +4,8 @@ # tools/calibrate; do not edit by hand. # Licensing: see the annotation in REUSE.toml. base_config: config/default.yaml -generated_at: '2026-07-22T19:14:30+00:00' -git_commit: 81a69806335b9eeba703c84240982fd8ba8d131d +generated_at: '2026-07-22T21:20:53+00:00' +git_commit: ac8ebe7e7bcd34da622fb4294165ea98aa4bb063 source: default structural_config: aggregation.irrigated_area_source: current @@ -1007,7 +1007,6 @@ structural_config: water.irrigation.consumed_fraction: 0.58 water.irrigation.eta_max: 5.0 water.irrigation.eta_min: 0.2 - water.supply.groundwater: true water.supply.groundwater_ceiling_factor: 3.0 water.supply.pumping_cost_usd_per_m3: 0.04 water.supply.scarcity_tiers: false diff --git a/data/curated/calibration/gbd-anchored/animal_cost.csv b/data/curated/calibration/gbd-anchored/animal_cost.csv index 79543384..baca7eb7 100644 --- a/data/curated/calibration/gbd-anchored/animal_cost.csv +++ b/data/curated/calibration/gbd-anchored/animal_cost.csv @@ -1,179 +1,179 @@ product,country,correction_bnusd_per_mt_feed -dairy,AFG,0.08214666694402695 -dairy,AGO,-0.10987724363803864 -dairy,ALB,0.026412979932501912 -dairy,ARE,-0.005148250726051629 -dairy,ARG,-0.16150102019309998 -dairy,ARM,0.020045739132910967 +dairy,AFG,0.08215003833174706 +dairy,AGO,-0.11083037778735161 +dairy,ALB,-0.20431764947716147 +dairy,ARE,-0.005329636042006314 +dairy,ARG,-0.16118013113737106 +dairy,ARM,0.021200379822403193 dairy,ASM,-0.0 -dairy,ATG,-0.12726226821541786 -dairy,AUS,-0.012923350557684898 -dairy,AUT,0.036639988189563155 -dairy,AZE,-0.0063932305201888084 -dairy,BDI,-0.091891560703516 -dairy,BEL,0.010255825705826283 -dairy,BEN,-0.14354928210377693 -dairy,BFA,-0.14950542151927948 -dairy,BGD,-0.02539757266640663 -dairy,BGR,0.08815251290798187 -dairy,BHS,-0.0868096835911274 -dairy,BIH,0.13570280373096466 -dairy,BLR,-0.02068411896470934 -dairy,BLZ,-0.12911782041192055 -dairy,BOL,-0.1563887521624565 -dairy,BRA,-0.1444319561123848 -dairy,BRB,-0.2410820871591568 -dairy,BRN,-0.0017232567770406604 -dairy,BTN,-0.01630973257124424 -dairy,BWA,-0.1484527923166752 -dairy,CAF,-0.1277887374162674 -dairy,CAN,-0.1138233095407486 -dairy,CHE,0.03528300253674388 -dairy,CHL,-0.1339343786239624 -dairy,CHN,0.21929922699928284 -dairy,CIV,-0.14677882194519043 -dairy,CMR,-0.15122009813785553 -dairy,COD,-0.1540760099887848 -dairy,COG,-0.1492903009057045 -dairy,COL,-0.17504166811704636 -dairy,COM,-0.13859135285019875 -dairy,CPV,-0.12097958102822304 -dairy,CRI,-0.06204695254564285 -dairy,CUB,-0.13943956047296524 -dairy,CYP,0.08251389861106873 -dairy,CZE,0.03506189538165927 -dairy,DEU,0.03866098169237375 -dairy,DJI,-0.02475464902818203 -dairy,DNK,0.05701321642845869 -dairy,DOM,-0.03578335791826248 -dairy,DZA,0.001464224886149168 -dairy,ECU,-0.17743946611881256 -dairy,EGY,-0.03198168333619833 -dairy,ERI,-0.0883121658116579 -dairy,ESP,-0.038572759833186865 -dairy,EST,0.03936504712328315 -dairy,ETH,-0.12410325556993484 -dairy,FIN,0.12222549319267273 -dairy,FJI,0.009359191171824932 -dairy,FRA,-0.024295165669173002 -dairy,GAB,-0.04205810837447643 -dairy,GBR,0.10139231383800507 -dairy,GEO,0.014037666842341423 -dairy,GHA,-0.09210006520152092 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-dairy,TTO,-0.11264410987496376 -dairy,TUN,-0.019329542759805918 -dairy,TUR,-0.013964010402560234 -dairy,TWN,0.1632743626832962 -dairy,TZA,-0.10778044909238815 -dairy,UGA,-0.14271050691604614 -dairy,UKR,0.018064737087115645 -dairy,URY,-0.16679321229457855 -dairy,USA,0.03219398856163025 -dairy,UZB,0.09240839257836342 -dairy,VEN,-0.17076891660690308 -dairy,VNM,-0.03854653798043728 -dairy,VUT,-0.0309450663626194 -dairy,YEM,-0.06133320927619934 -dairy,ZAF,-0.1103108637034893 -dairy,ZMB,-0.10638464614748955 -dairy,ZWE,-0.371809259057045 +dairy,GUY,-0.12800442799925804 +dairy,HND,-0.1271578036248684 +dairy,HRV,0.014650707133114338 +dairy,HTI,-0.03952471911907196 +dairy,HUN,0.02818544115871191 +dairy,IDN,0.03230041987262666 +dairy,IND,-0.028696982190012932 +dairy,IRL,-0.055097468197345734 +dairy,IRN,0.053757319226861 +dairy,IRQ,-0.057800618931651115 +dairy,ISL,-0.10245237872004509 +dairy,ISR,-0.11273118481040001 +dairy,ITA,-0.0008560987189412117 +dairy,JAM,-0.05655079707503319 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dairy-buffalo,ALB,-0.0 @@ -189,8 +189,8 @@ dairy-buffalo,BDI,-0.0 dairy-buffalo,BEL,-0.0 dairy-buffalo,BEN,-0.0 dairy-buffalo,BFA,-0.0 -dairy-buffalo,BGD,-0.06616293266415596 -dairy-buffalo,BGR,0.0319812037050724 +dairy-buffalo,BGD,-0.1165320910513401 +dairy-buffalo,BGR,0.03203514218330383 dairy-buffalo,BHS,-0.0 dairy-buffalo,BIH,-0.0 dairy-buffalo,BLR,-0.0 @@ -198,14 +198,14 @@ dairy-buffalo,BLZ,-0.0 dairy-buffalo,BOL,-0.0 dairy-buffalo,BRA,-0.0 dairy-buffalo,BRB,-0.0 -dairy-buffalo,BRN,-0.06290468201041222 +dairy-buffalo,BRN,-0.06369943358004093 dairy-buffalo,BTN,-0.0 dairy-buffalo,BWA,-0.0 dairy-buffalo,CAF,-0.0 dairy-buffalo,CAN,-0.0 dairy-buffalo,CHE,-0.0 dairy-buffalo,CHL,-0.0 -dairy-buffalo,CHN,-0.046801965683698654 +dairy-buffalo,CHN,-0.04868816025555134 dairy-buffalo,CIV,-0.0 dairy-buffalo,CMR,-0.0 dairy-buffalo,COD,-0.0 @@ -223,7 +223,7 @@ dairy-buffalo,DNK,-0.0 dairy-buffalo,DOM,-0.0 dairy-buffalo,DZA,-0.0 dairy-buffalo,ECU,-0.0 -dairy-buffalo,EGY,-0.010649620555341244 +dairy-buffalo,EGY,-0.010312838014215231 dairy-buffalo,ERI,-0.0 dairy-buffalo,ESP,-0.0 dairy-buffalo,EST,-0.0 @@ -239,7 +239,7 @@ dairy-buffalo,GIN,-0.0 dairy-buffalo,GMB,-0.0 dairy-buffalo,GNB,-0.0 dairy-buffalo,GNQ,-0.0 -dairy-buffalo,GRC,-0.01851563574746251 +dairy-buffalo,GRC,-0.019594809971749783 dairy-buffalo,GRD,-0.0 dairy-buffalo,GTM,-0.0 dairy-buffalo,GUF,-0.0 @@ -248,14 +248,14 @@ dairy-buffalo,HND,-0.0 dairy-buffalo,HRV,-0.0 dairy-buffalo,HTI,-0.0 dairy-buffalo,HUN,-0.0 -dairy-buffalo,IDN,-0.0981048010289669 -dairy-buffalo,IND,-0.02791750803589821 +dairy-buffalo,IDN,-0.10016023740172386 +dairy-buffalo,IND,-0.02869695983827114 dairy-buffalo,IRL,-0.0 -dairy-buffalo,IRN,0.0358537957072258 -dairy-buffalo,IRQ,-0.06018510460853577 +dairy-buffalo,IRN,0.03769579343497753 +dairy-buffalo,IRQ,-0.06143476255238056 dairy-buffalo,ISL,-0.0 dairy-buffalo,ISR,-0.0 -dairy-buffalo,ITA,0.06379183009266853 +dairy-buffalo,ITA,0.06442580744624138 dairy-buffalo,JAM,-0.0 dairy-buffalo,JOR,-0.0 dairy-buffalo,JPN,-0.0 @@ -268,7 +268,7 @@ dairy-buffalo,LAO,-0.0 dairy-buffalo,LBN,-0.0 dairy-buffalo,LBR,-0.0 dairy-buffalo,LBY,-0.0 -dairy-buffalo,LKA,-0.051092286594212055 +dairy-buffalo,LKA,-0.050810471177101135 dairy-buffalo,LSO,-0.0 dairy-buffalo,LTU,-0.0 dairy-buffalo,LUX,-0.0 @@ -280,24 +280,24 @@ dairy-buffalo,MEX,-0.0 dairy-buffalo,MKD,-0.0 dairy-buffalo,MLI,-0.0 dairy-buffalo,MLT,-0.0 -dairy-buffalo,MMR,-0.09994760528206825 +dairy-buffalo,MMR,-0.10159696638584137 dairy-buffalo,MNE,-0.0 dairy-buffalo,MNG,-0.0 dairy-buffalo,MOZ,-0.0 dairy-buffalo,MRT,-0.0 dairy-buffalo,MUS,-0.0 dairy-buffalo,MWI,-0.0 -dairy-buffalo,MYS,-0.04134524893015623 +dairy-buffalo,MYS,-0.01613876549527049 dairy-buffalo,NAM,-0.0 dairy-buffalo,NER,-0.0 dairy-buffalo,NGA,-0.0 dairy-buffalo,NIC,-0.0 dairy-buffalo,NLD,-0.0 dairy-buffalo,NOR,-0.0 -dairy-buffalo,NPL,-0.027701597660779953 +dairy-buffalo,NPL,-0.028188431635499 dairy-buffalo,NZL,-0.0 dairy-buffalo,OMN,-0.0 -dairy-buffalo,PAK,0.04260141961276531 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-meat-sheep,NPL,-0.05352458171546459 +meat-sheep,NLD,0.011312925722450018 +meat-sheep,NOR,0.05078548099845648 +meat-sheep,NPL,-0.05472451634705067 meat-sheep,NZL,0.0005488632596097887 -meat-sheep,OMN,-0.10465916991233826 -meat-sheep,PAK,-0.07066883333027363 +meat-sheep,OMN,-0.1071966104209423 +meat-sheep,PAK,-0.07186877168715 meat-sheep,PAN,-0.0 -meat-sheep,PER,-0.03324451670050621 -meat-sheep,PHL,-0.08635231107473373 -meat-sheep,PNG,-0.026137974113225937 -meat-sheep,POL,0.011744205374270678 +meat-sheep,PER,-0.03458201885223389 +meat-sheep,PHL,-0.08888974413275719 +meat-sheep,PNG,-0.027475474402308464 +meat-sheep,POL,0.009498314931988716 meat-sheep,PRI,-0.01051483117043972 -meat-sheep,PRT,0.098675437271595 +meat-sheep,PRT,0.09777285531163216 meat-sheep,PRY,-0.007636617810931057 -meat-sheep,PSE,-0.055522702634334564 -meat-sheep,ROU,-0.016659659333527088 -meat-sheep,RUS,0.012786585837602615 -meat-sheep,RWA,-0.038569577038288116 -meat-sheep,SAU,-0.07191868126392365 -meat-sheep,SDN,-0.05038566328585148 -meat-sheep,SEN,-0.07243901304900646 +meat-sheep,PSE,-0.05806013569235802 +meat-sheep,ROU,-0.019197095185518265 +meat-sheep,RUS,0.010249147191643715 +meat-sheep,RWA,-0.03976951166987419 +meat-sheep,SAU,-0.07445611618459225 +meat-sheep,SDN,-0.05158560164272785 +meat-sheep,SEN,-0.07431602291762829 meat-sheep,SLB,-0.0 -meat-sheep,SLE,-0.07532412931323051 -meat-sheep,SLV,-0.03697171760722995 -meat-sheep,SOM,-0.05844439938664436 -meat-sheep,SRB,0.08748101070523262 -meat-sheep,SSD,-0.040212699212133884 -meat-sheep,STP,-0.002143390476703644 -meat-sheep,SUR,0.01047318521887064 -meat-sheep,SVK,0.013529479503631592 -meat-sheep,SVN,0.08259506896138191 -meat-sheep,SWE,0.08849150314927101 -meat-sheep,SWZ,-0.09922288358211517 -meat-sheep,SYR,-0.09117881953716278 -meat-sheep,TCD,-0.07071537710726261 -meat-sheep,TGO,-0.06159967742860317 -meat-sheep,THA,-0.0667047593742609 -meat-sheep,TJK,-0.08428248763084412 -meat-sheep,TKM,-0.12523771077394485 -meat-sheep,TLS,-0.0782748181372881 +meat-sheep,SLE,-0.07786156237125397 +meat-sheep,SLV,-0.038309216033667326 +meat-sheep,SOM,-0.05964433774352074 +meat-sheep,SRB,0.08614351227879524 +meat-sheep,SSD,-0.04141263384371996 +meat-sheep,STP,-0.003480890765786171 +meat-sheep,SUR,0.009273248724639416 +meat-sheep,SVK,0.01099204272031784 +meat-sheep,SVN,0.08125756867229939 +meat-sheep,SWE,0.08715400099754333 +meat-sheep,SWZ,-0.10176033340394497 +meat-sheep,SYR,-0.09305813163518906 +meat-sheep,TCD,-0.07191531173884869 +meat-sheep,TGO,-0.06413711234927177 +meat-sheep,THA,-0.06790469773113728 +meat-sheep,TJK,-0.08681992813944817 +meat-sheep,TKM,-0.1277751512825489 +meat-sheep,TLS,-0.07947475276887417 meat-sheep,TTO,0.0053625027649104595 -meat-sheep,TUN,-0.05145137198269367 -meat-sheep,TUR,-0.05173586402088404 +meat-sheep,TUN,-0.05278887413442135 +meat-sheep,TUR,-0.054273298010230064 meat-sheep,TWN,-0.0 -meat-sheep,TZA,-0.06165468320250511 -meat-sheep,UGA,-0.09359623864293098 -meat-sheep,UKR,0.0233253741171211 +meat-sheep,TZA,-0.06285461783409119 +meat-sheep,UGA,-0.09613367170095444 +meat-sheep,UKR,0.020787938963621855 meat-sheep,URY,-0.003583780489861965 -meat-sheep,USA,0.005756152328103781 -meat-sheep,UZB,-0.07489606738090515 -meat-sheep,VEN,-0.04798725713044405 -meat-sheep,VNM,-0.08581174910068512 -meat-sheep,VUT,-0.0925622433423996 -meat-sheep,YEM,-0.055183399468660355 -meat-sheep,ZAF,-0.0121488724835217 -meat-sheep,ZMB,-0.050897425040602684 -meat-sheep,ZWE,-0.33774327486753464 +meat-sheep,USA,0.004418652039021254 +meat-sheep,UZB,-0.0774335078895092 +meat-sheep,VEN,-0.04932475555688143 +meat-sheep,VNM,-0.08834918215870857 +meat-sheep,VUT,-0.09389974549412727 +meat-sheep,YEM,-0.05638333596289158 +meat-sheep,ZAF,-0.05348121374845505 +meat-sheep,ZMB,-0.052097368985414505 +meat-sheep,ZWE,-0.3426752910017967 diff --git a/data/curated/calibration/gbd-anchored/crop_cost.csv b/data/curated/calibration/gbd-anchored/crop_cost.csv index cdf5f32d..41850510 100644 --- a/data/curated/calibration/gbd-anchored/crop_cost.csv +++ b/data/curated/calibration/gbd-anchored/crop_cost.csv @@ -1,178 +1,178 @@ crop,country,correction_bnusd_per_mha alfalfa,AFG,-0.3613618314266205 alfalfa,AGO,-0.9381504952907562 -alfalfa,ALB,-0.5532752722501755 +alfalfa,ALB,-0.1561402529478073 alfalfa,ARE,-0.18636378645896912 -alfalfa,ARG,-0.4758569896221161 +alfalfa,ARG,-0.4753821790218353 alfalfa,ARM,-0.0 alfalfa,ATG,-7.5 -alfalfa,AUS,0.02438315376639366 +alfalfa,AUS,0.9802871346473694 alfalfa,AUT,-0.8089405298233032 -alfalfa,AZE,0.33599771559238434 +alfalfa,AZE,0.6302368938922882 alfalfa,BDI,-0.8992443978786469 alfalfa,BEL,0.045041752979159355 -alfalfa,BEN,0.38562148809432983 +alfalfa,BEN,0.40457744896411896 alfalfa,BFA,-0.0 -alfalfa,BGD,-0.7334521114826202 -alfalfa,BGR,-0.25662170350551605 -alfalfa,BHS,-0.6647240519523621 -alfalfa,BIH,3.645371161401272 -alfalfa,BLR,-2.7995163053274155 +alfalfa,BGD,-1.1770754158496857 +alfalfa,BGR,-0.25608454644680023 +alfalfa,BHS,-4.099594205617905 +alfalfa,BIH,3.651162840425968 +alfalfa,BLR,-2.841612681746483 alfalfa,BLZ,-0.010105629451572895 alfalfa,BOL,-0.33576732873916626 alfalfa,BRA,-0.656684398651123 -alfalfa,BRB,1.8175461292266846 +alfalfa,BRB,1.861784279346466 alfalfa,BRN,-0.8346583247184753 -alfalfa,BTN,-0.2557598948478699 +alfalfa,BTN,-0.10611472174787195 alfalfa,BWA,-7.5 -alfalfa,CAF,-0.6662335991859436 -alfalfa,CAN,-0.4989238977432251 -alfalfa,CHE,-0.14492646604776382 +alfalfa,CAF,-0.9728804230690002 +alfalfa,CAN,-0.51876500248909 +alfalfa,CHE,-0.11861268803477287 alfalfa,CHL,-0.868007481098175 -alfalfa,CHN,0.10536020249128342 +alfalfa,CHN,0.1337210088968277 alfalfa,CIV,7.5 -alfalfa,CMR,0.4638580083847046 +alfalfa,CMR,0.47671011090278625 alfalfa,COD,-0.10928117483854294 alfalfa,COG,-0.09945236518979073 alfalfa,COL,-0.1985142081975937 -alfalfa,COM,0.313687726855278 +alfalfa,COM,0.33056776225566864 alfalfa,CPV,-7.5 alfalfa,CRI,-0.22407843172550201 alfalfa,CUB,-0.2862354665994644 -alfalfa,CYP,2.298451542854309 +alfalfa,CYP,2.841817617416382 alfalfa,CZE,-0.5457680821418762 alfalfa,DEU,-0.19640730321407318 alfalfa,DJI,-0.835690438747406 -alfalfa,DNK,6.0882463455200195 +alfalfa,DNK,6.192570209503174 alfalfa,DOM,-1.1007803976535797 -alfalfa,DZA,0.14527757093310356 +alfalfa,DZA,0.24171801656484604 alfalfa,ECU,-0.07392867282032967 -alfalfa,EGY,1.445999264717102 +alfalfa,EGY,1.8034262657165527 alfalfa,ERI,-0.8745495080947876 -alfalfa,ESP,-1.1559807658195496 +alfalfa,ESP,-1.1736907958984375 alfalfa,EST,-0.119085893034935 alfalfa,ETH,-0.8244766592979431 alfalfa,FIN,-0.0 -alfalfa,FJI,-0.5054319649934769 +alfalfa,FJI,-0.50013667345047 alfalfa,FRA,-0.31077052652835846 alfalfa,GAB,-0.8707610964775085 -alfalfa,GBR,0.018968021497130394 +alfalfa,GBR,0.03519535902887583 alfalfa,GEO,-0.5318324565887451 alfalfa,GHA,-0.9061825275421143 alfalfa,GIN,-0.8761205673217773 alfalfa,GMB,-7.5 alfalfa,GNB,-0.0 alfalfa,GNQ,-0.7592925727367401 -alfalfa,GRC,-0.7339456975460052 -alfalfa,GRD,0.12151048332452774 +alfalfa,GRC,-0.6445825695991516 +alfalfa,GRD,0.13762561976909637 alfalfa,GTM,-0.5668327510356903 alfalfa,GUF,-2.7332428693771362 alfalfa,GUY,-0.07167529128491879 alfalfa,HND,-0.3479782044887543 -alfalfa,HRV,-4.087017804384232 +alfalfa,HRV,-4.084043055772781 alfalfa,HTI,-0.8184686303138733 -alfalfa,HUN,-0.7068566083908081 +alfalfa,HUN,-2.6375420689582825 alfalfa,IDN,-0.14206960052251816 -alfalfa,IND,2.307610034942627 +alfalfa,IND,2.3430310487747192 alfalfa,IRL,-0.5063982009887695 alfalfa,IRN,-1.2234086990356445 -alfalfa,IRQ,1.670216679573059 +alfalfa,IRQ,1.6999701261520386 alfalfa,ISL,-0.8342201411724091 -alfalfa,ISR,1.2119269371032715 -alfalfa,ITA,0.3164842128753662 -alfalfa,JAM,1.4212384223937988 -alfalfa,JOR,0.44870468974113464 +alfalfa,ISR,1.2351350784301758 +alfalfa,ITA,0.3886013627052307 +alfalfa,JAM,1.459454894065857 +alfalfa,JOR,0.6564856767654419 alfalfa,JPN,-0.03383052349090576 -alfalfa,KAZ,-0.5768600106239319 +alfalfa,KAZ,-0.5224663019180298 alfalfa,KEN,-0.4995366930961609 alfalfa,KGZ,-0.8171840310096741 alfalfa,KHM,-0.2528349757194519 -alfalfa,KOR,-0.10150226205587387 -alfalfa,LAO,3.6615079939365387 -alfalfa,LBN,2.6489391326904297 +alfalfa,KOR,-0.09110401570796967 +alfalfa,LAO,3.4008332788944244 +alfalfa,LBN,2.7185308933258057 alfalfa,LBR,-0.12358545139431953 -alfalfa,LBY,-1.7812474370002747 -alfalfa,LKA,-0.346634104847908 +alfalfa,LBY,-0.8150710463523865 +alfalfa,LKA,-0.8748966157436371 alfalfa,LSO,-0.38055141270160675 alfalfa,LTU,-0.029261786490678787 -alfalfa,LUX,-0.3940722197294235 -alfalfa,LVA,-0.9987483620643616 -alfalfa,MAR,1.1028937697410583 -alfalfa,MDA,-0.19866261631250381 +alfalfa,LUX,-0.3734655976295471 +alfalfa,LVA,-0.5740866959095001 +alfalfa,MAR,-0.0 +alfalfa,MDA,-0.37392619252204895 alfalfa,MDG,-0.26456888020038605 -alfalfa,MEX,-0.18802402913570404 -alfalfa,MKD,-0.16083867847919464 +alfalfa,MEX,-0.19758720695972443 +alfalfa,MKD,-0.11725929193198681 alfalfa,MLI,-7.5 alfalfa,MLT,-7.5 -alfalfa,MMR,3.5661709308624268 -alfalfa,MNE,0.15972304320894182 -alfalfa,MNG,-0.8295928537845612 +alfalfa,MMR,3.607537031173706 +alfalfa,MNE,0.2056301822885871 +alfalfa,MNG,-0.8294159471988678 alfalfa,MOZ,-0.6108028888702393 -alfalfa,MRT,-0.9963793754577637 +alfalfa,MRT,-0.6804739832878113 alfalfa,MUS,-0.7995986342430115 alfalfa,MWI,-0.453051894903183 -alfalfa,MYS,-0.28343406319618225 +alfalfa,MYS,-0.7145838141441345 alfalfa,NAM,-7.5 -alfalfa,NER,-0.17374803870916367 +alfalfa,NER,-0.16345621272921562 alfalfa,NGA,-0.9605015814304352 alfalfa,NIC,-1.8630978465080261 -alfalfa,NLD,6.977464199066162 +alfalfa,NLD,6.91517186164856 alfalfa,NOR,-0.0 -alfalfa,NPL,-0.9368138611316681 +alfalfa,NPL,-0.9361520409584045 alfalfa,NZL,-0.272330105304718 alfalfa,OMN,-0.0 alfalfa,PAK,-0.8301001787185669 alfalfa,PAN,-1.0878176093101501 -alfalfa,PER,-0.8857477307319641 +alfalfa,PER,-0.891121119260788 alfalfa,PHL,-0.07465366646647453 alfalfa,PNG,-0.18700392544269562 -alfalfa,POL,-0.5511676073074341 +alfalfa,POL,-0.5392315685749054 alfalfa,PRI,-0.1749081201851368 -alfalfa,PRT,-0.16223414987325668 +alfalfa,PRT,-0.10587922856211662 alfalfa,PRY,-0.2994462549686432 -alfalfa,PSE,0.8374344706535339 -alfalfa,ROU,-0.1342918463051319 -alfalfa,RUS,-0.37536077201366425 +alfalfa,PSE,0.8553124070167542 +alfalfa,ROU,-0.16130127012729645 +alfalfa,RUS,-0.37581662833690643 alfalfa,RWA,-0.22617708891630173 -alfalfa,SAU,0.38244058191776276 -alfalfa,SDN,-0.8540876507759094 +alfalfa,SAU,0.5365370810031891 +alfalfa,SDN,-0.8521369099617004 alfalfa,SEN,-0.0 alfalfa,SLB,-0.6203618347644806 alfalfa,SLE,-0.49844561517238617 -alfalfa,SLV,0.8268271088600159 +alfalfa,SLV,0.8478626608848572 alfalfa,SOM,-0.919923722743988 -alfalfa,SRB,0.10355705581605434 +alfalfa,SRB,0.13105096481740475 alfalfa,SSD,-0.8797294795513153 -alfalfa,STP,0.8139723837375641 -alfalfa,SUR,-0.7841159105300903 -alfalfa,SVK,-0.46134044229984283 -alfalfa,SVN,-0.5659743845462799 +alfalfa,STP,0.8389132916927338 +alfalfa,SUR,-0.7872246205806732 +alfalfa,SVK,-0.6408847570419312 +alfalfa,SVN,-0.6688656508922577 alfalfa,SWE,-0.0 alfalfa,SWZ,-0.23123173415660858 alfalfa,SYR,-0.0 alfalfa,TCD,-0.9309031963348389 alfalfa,TGO,-0.03396269865334034 alfalfa,THA,-0.6562699973583221 -alfalfa,TJK,-0.2675362601876259 +alfalfa,TJK,-0.25877534598112106 alfalfa,TKM,-0.0 alfalfa,TLS,-0.038952913135290146 alfalfa,TTO,-0.7199581563472748 -alfalfa,TUN,0.12764467298984528 -alfalfa,TUR,2.8622708320617676 -alfalfa,TWN,0.5775642096996307 +alfalfa,TUN,0.1360294669866562 +alfalfa,TUR,2.9269274473190308 +alfalfa,TWN,0.5903020203113556 alfalfa,TZA,-0.6803342700004578 -alfalfa,UGA,-0.2331766113638878 -alfalfa,UKR,-0.28949199616909027 +alfalfa,UGA,-0.23317663371562958 +alfalfa,UKR,-0.4236515909433365 alfalfa,URY,-0.6376457810401917 -alfalfa,USA,0.01593978051096201 -alfalfa,UZB,-1.765395164489746 -alfalfa,VEN,-0.2790147513151169 -alfalfa,VNM,1.978816531598568 +alfalfa,USA,0.04668472521007061 +alfalfa,UZB,-1.764338731765747 +alfalfa,VEN,-0.26397500932216644 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dryland-rice,ZAF,7.5 -dryland-rice,ZMB,-1.152294397354126 +dryland-rice,ZMB,-1.1493853330612183 dryland-rice,ZWE,7.5 foxtail-millet,AFG,-0.5469042658805847 foxtail-millet,AGO,-0.40684472024440765 foxtail-millet,ARG,-0.4919593334197998 foxtail-millet,ARM,-0.0 foxtail-millet,AUS,7.5 -foxtail-millet,AZE,-4.385242700576782 +foxtail-millet,AZE,-4.347458600997925 foxtail-millet,BDI,-0.48601867258548737 foxtail-millet,BEN,-0.5482176244258881 foxtail-millet,BFA,-0.3707796037197113 -foxtail-millet,BGD,-0.488968625664711 +foxtail-millet,BGD,-0.36577150225639343 foxtail-millet,BGR,-0.0 foxtail-millet,BLR,-0.3229052722454071 foxtail-millet,BOL,-0.0 foxtail-millet,BRA,-0.0 -foxtail-millet,BTN,-4.268095791339874 -foxtail-millet,BWA,-0.42977622151374817 +foxtail-millet,BTN,-4.26423841714859 +foxtail-millet,BWA,-0.4305408149957657 foxtail-millet,CAF,-7.5 foxtail-millet,CAN,-0.0 foxtail-millet,CHE,-2.6688425540924072 -foxtail-millet,CHN,-0.7178941369056702 +foxtail-millet,CHN,-0.7084050178527832 foxtail-millet,CIV,-0.577817440032959 foxtail-millet,CMR,-0.4593006670475006 foxtail-millet,COD,-0.4491131752729416 @@ -2372,7 +2372,7 @@ foxtail-millet,DEU,7.5 foxtail-millet,DZA,-0.0 foxtail-millet,ERI,-0.4153728485107422 foxtail-millet,ETH,-0.7273882031440735 -foxtail-millet,FRA,3.240796685218811 +foxtail-millet,FRA,3.2705741226673126 foxtail-millet,GAB,-0.0 foxtail-millet,GEO,-0.0 foxtail-millet,GHA,-0.6622898280620575 @@ -2381,19 +2381,19 @@ foxtail-millet,GMB,-0.46135395765304565 foxtail-millet,GNB,-0.4469609558582306 foxtail-millet,GRC,-0.0 foxtail-millet,HUN,7.5 -foxtail-millet,IND,-0.49366603791713715 +foxtail-millet,IND,-0.4926038831472397 foxtail-millet,IRN,-1.0512065291404724 foxtail-millet,IRQ,-0.8328883051872253 -foxtail-millet,KAZ,-0.29122042655944824 +foxtail-millet,KAZ,-0.2774594873189926 foxtail-millet,KEN,-0.49875615537166595 foxtail-millet,KGZ,-0.7159605026245117 foxtail-millet,KHM,-0.0 foxtail-millet,KOR,-0.40312229096889496 foxtail-millet,LAO,0.0 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-sunflower,KAZ,-0.08775471895933151 -sunflower,KEN,-0.036859869956970215 -sunflower,KGZ,-0.3476056754589081 -sunflower,KHM,3.5146009027957916 -sunflower,LAO,3.531338721513748 +sunflower,KAZ,0.05716937966644764 +sunflower,KEN,-0.0373603655025363 +sunflower,KGZ,-0.348459392786026 +sunflower,KHM,3.5143134146928787 +sunflower,LAO,3.5310188978910446 sunflower,LBN,7.5 -sunflower,LSO,0.10661692172288895 -sunflower,LTU,-0.2611251324415207 -sunflower,LUX,-0.20745299942791462 -sunflower,LVA,3.7092783339321613 -sunflower,MAR,-0.9136098921298981 -sunflower,MDA,0.313670389354229 -sunflower,MEX,0.10448036342859268 -sunflower,MKD,-0.04516573529690504 -sunflower,MMR,-0.4252316951751709 -sunflower,MNE,3.8001424446702003 -sunflower,MNG,-0.15350359305739403 -sunflower,MOZ,0.16346706449985504 -sunflower,MWI,3.552095517516136 +sunflower,LSO,0.10490316338837147 +sunflower,LTU,-0.26222405582666397 +sunflower,LUX,-0.221726443618536 +sunflower,LVA,3.708535000681877 +sunflower,MAR,-0.9371745586395264 +sunflower,MDA,0.07423880975693464 +sunflower,MEX,0.10333940386772156 +sunflower,MKD,0.06687560677528381 +sunflower,MMR,-0.42589071393013 +sunflower,MNE,3.7991978339850903 +sunflower,MNG,-0.15464574098587036 +sunflower,MOZ,0.16287297010421753 +sunflower,MWI,3.5517296344041824 sunflower,MYS,-0.0 sunflower,NAM,7.5 -sunflower,NLD,-1.3348091393709183 -sunflower,NPL,3.5246463119983673 -sunflower,PAK,-0.23967017233371735 +sunflower,NLD,-1.336266428232193 +sunflower,NPL,3.524331197142601 +sunflower,PAK,-0.24065732955932617 sunflower,PER,-0.0 -sunflower,POL,3.7455567801371217 -sunflower,PRT,-0.05145086720585823 -sunflower,PRY,-0.3578992486000061 -sunflower,PSE,0.4808729737997055 -sunflower,ROU,0.3973245918750763 -sunflower,RUS,0.18906638026237488 -sunflower,RWA,-0.5120755136013031 -sunflower,SDN,-0.5548676252365112 +sunflower,POL,3.7447272120043635 +sunflower,PRT,-0.052757419645786285 +sunflower,PRY,-0.3588106036186218 +sunflower,PSE,0.4780798852443695 +sunflower,ROU,0.38551610708236694 +sunflower,RUS,0.18756256997585297 +sunflower,RWA,-0.5125937163829803 +sunflower,SDN,-0.5657435059547424 sunflower,SOM,-0.0 -sunflower,SRB,0.7378244400024414 -sunflower,SSD,-0.27872395515441895 +sunflower,SRB,0.7522255629301071 +sunflower,SSD,-0.2796476185321808 sunflower,SUR,-0.0 -sunflower,SVK,0.3458826835267246 -sunflower,SVN,0.25513507425785065 -sunflower,SWZ,-0.2618430554866791 -sunflower,SYR,-0.3437250256538391 -sunflower,TCD,3.4842810332775116 -sunflower,THA,-0.14310326427221298 -sunflower,TJK,-0.28524112701416016 -sunflower,TKM,0.061943624168634415 -sunflower,TUN,-0.40580035746097565 -sunflower,TUR,-0.46466635167598724 -sunflower,TWN,0.9086529612541199 -sunflower,TZA,0.0947735607624054 -sunflower,UGA,-0.44752515852451324 -sunflower,UKR,-0.26756829023361206 -sunflower,URY,-0.2043401077389717 -sunflower,USA,0.01930211391299963 -sunflower,UZB,-0.5691335797309875 -sunflower,VEN,-0.5151329934597015 +sunflower,SVK,-0.0424871533177793 +sunflower,SVN,0.2530328556895256 +sunflower,SWZ,-0.2628379911184311 +sunflower,SYR,-0.3446008265018463 +sunflower,TCD,3.484082579612732 +sunflower,THA,-0.14357343316078186 +sunflower,TJK,-0.28627362847328186 +sunflower,TKM,0.06030575558543205 +sunflower,TUN,-0.4065604954957962 +sunflower,TUR,-0.4666791707277298 +sunflower,TWN,0.9054418206214905 +sunflower,TZA,0.09410687536001205 +sunflower,UGA,-0.4481782168149948 +sunflower,UKR,-0.33214548230171204 +sunflower,URY,-0.20556289702653885 +sunflower,USA,0.040569182485342026 +sunflower,UZB,-0.5703222751617432 +sunflower,VEN,-0.5155743360519409 sunflower,VNM,-0.0 -sunflower,ZAF,-0.2555319368839264 -sunflower,ZMB,-0.5946124196052551 -sunflower,ZWE,-0.07656265050172806 +sunflower,ZAF,-0.22984914481639862 +sunflower,ZMB,-0.5949535369873047 +sunflower,ZWE,-0.07692591100931168 sweet-potato,AFG,-1.9192432761192322 -sweet-potato,AGO,-3.198032855987549 -sweet-potato,ALB,-1.8855436444282532 +sweet-potato,AGO,-3.2094719409942627 +sweet-potato,ALB,-1.5460264086723328 sweet-potato,ARE,-0.9062895774841309 -sweet-potato,ARG,-0.0789831206202507 +sweet-potato,ARG,-0.10665104538202286 sweet-potato,ARM,-1.5438308119773865 sweet-potato,ATG,-4.1917619705200195 sweet-potato,AUS,7.5 -sweet-potato,AUT,-0.7854494452476501 -sweet-potato,AZE,-2.340116858482361 +sweet-potato,AUT,-0.8321494162082672 +sweet-potato,AZE,-2.2849515676498413 sweet-potato,BDI,-0.5438300371170044 sweet-potato,BEL,-0.0 -sweet-potato,BEN,-1.0486331582069397 +sweet-potato,BEN,-1.6471242308616638 sweet-potato,BFA,-3.103135347366333 -sweet-potato,BGD,1.0177840888500214 +sweet-potato,BGD,0.8543735444545746 sweet-potato,BGR,-1.508312165737152 sweet-potato,BHS,-3.5734505653381348 sweet-potato,BIH,2.5080337524414062 -sweet-potato,BLR,-3.852271795272827 +sweet-potato,BLR,-3.8943681716918945 sweet-potato,BLZ,-2.782684326171875 -sweet-potato,BOL,-0.29977747797966003 -sweet-potato,BRA,-0.9279706478118896 +sweet-potato,BOL,-0.3270563781261444 +sweet-potato,BRA,-1.0339245796203613 sweet-potato,BRB,-7.5 sweet-potato,BRN,-7.5 sweet-potato,BTN,7.5 @@ -5113,47 +5113,47 @@ sweet-potato,COM,-3.5574982166290283 sweet-potato,CPV,-7.5 sweet-potato,CRI,3.603176921606064 sweet-potato,CUB,-2.4839179515838623 -sweet-potato,CYP,-4.3751402497291565 +sweet-potato,CYP,-4.174723595380783 sweet-potato,CZE,-0.27531448006629944 sweet-potato,DEU,-0.0 sweet-potato,DNK,-0.0 -sweet-potato,DOM,-1.865368664264679 -sweet-potato,DZA,-1.2789065837860107 +sweet-potato,DOM,-2.00942862033844 +sweet-potato,DZA,-1.030538558959961 sweet-potato,ECU,0.7807817161083221 -sweet-potato,EGY,-1.7717967629432678 +sweet-potato,EGY,-1.885290265083313 sweet-potato,ERI,1.8305708169937134 sweet-potato,ESP,-7.5 sweet-potato,EST,-0.7400773763656616 sweet-potato,ETH,-2.280057907104492 sweet-potato,FIN,-1.2138773202896118 sweet-potato,FJI,-4.901826858520508 -sweet-potato,FRA,0.6528796255588531 +sweet-potato,FRA,0.6889048218727112 sweet-potato,GAB,-3.7182594537734985 -sweet-potato,GBR,-0.5943705625832081 -sweet-potato,GEO,-2.5016039609909058 +sweet-potato,GBR,-0.5930698588490486 +sweet-potato,GEO,-2.5010619163513184 sweet-potato,GHA,-0.46097269654273987 -sweet-potato,GIN,0.6378686428070068 +sweet-potato,GIN,-1.4446233659982681 sweet-potato,GMB,3.0768012404441833 sweet-potato,GNB,-3.3054310083389282 sweet-potato,GNQ,-3.4353625774383545 -sweet-potato,GRC,-2.119258403778076 +sweet-potato,GRC,-2.013211250305176 sweet-potato,GRD,-7.5 sweet-potato,GTM,-2.3480527997016907 -sweet-potato,GUF,-1.9435502886772156 +sweet-potato,GUF,-2.186120867729187 sweet-potato,GUY,0.0 -sweet-potato,HND,-1.5886735320091248 -sweet-potato,HRV,-4.954775929450989 +sweet-potato,HND,-1.5979636907577515 +sweet-potato,HRV,-3.0181812047958374 sweet-potato,HTI,-2.8239306211471558 sweet-potato,HUN,-1.2691103219985962 sweet-potato,IDN,-1.1633429527282715 -sweet-potato,IND,-0.908936619758606 +sweet-potato,IND,-0.9081221222877502 sweet-potato,IRL,-0.0 sweet-potato,IRN,-0.3400079905986786 sweet-potato,IRQ,-0.3500778526067734 sweet-potato,ISR,-7.5 -sweet-potato,ITA,-1.546323299407959 +sweet-potato,ITA,-1.990699052810669 sweet-potato,JAM,-7.5 -sweet-potato,JOR,-0.5413439720869064 +sweet-potato,JOR,-0.21019193530082703 sweet-potato,JPN,-7.5 sweet-potato,KAZ,-1.984000027179718 sweet-potato,KEN,-2.5283477306365967 @@ -5161,23 +5161,23 @@ sweet-potato,KGZ,-2.4194066524505615 sweet-potato,KHM,-2.6620535850524902 sweet-potato,KOR,-1.4717452824115753 sweet-potato,LAO,-1.55629962682724 -sweet-potato,LBN,-0.98282390832901 +sweet-potato,LBN,-2.2414374351501465 sweet-potato,LBR,-2.6142382621765137 -sweet-potato,LBY,-2.442838668823242 +sweet-potato,LBY,-1.0668233633041382 sweet-potato,LKA,-0.9270695447921753 sweet-potato,LSO,-1.8748398423194885 sweet-potato,LTU,-1.863678753376007 sweet-potato,LUX,-7.5 sweet-potato,LVA,-5.157782196998596 -sweet-potato,MAR,-1.679243564605713 +sweet-potato,MAR,-0.936816394329071 sweet-potato,MDA,-2.651017904281616 sweet-potato,MDG,-3.2593929767608643 sweet-potato,MEX,-2.5737690925598145 -sweet-potato,MKD,-1.9669275283813477 +sweet-potato,MKD,-1.9618785977363586 sweet-potato,MLI,-1.404753565788269 sweet-potato,MLT,-7.5 sweet-potato,MMR,-1.904980182647705 -sweet-potato,MNE,-1.8795904517173767 +sweet-potato,MNE,-1.8785265684127808 sweet-potato,MNG,-7.5 sweet-potato,MOZ,-1.2957948446273804 sweet-potato,MRT,-3.3117278814315796 @@ -5185,17 +5185,17 @@ sweet-potato,MUS,-5.612195730209351 sweet-potato,MWI,2.0278756618499756 sweet-potato,MYS,1.7713966369628906 sweet-potato,NAM,-2.5317625999450684 -sweet-potato,NER,-1.5427520871162415 +sweet-potato,NER,-2.0824800729751587 sweet-potato,NGA,-3.5510075092315674 -sweet-potato,NIC,-2.5846318006515503 -sweet-potato,NLD,-0.8278494775295258 +sweet-potato,NIC,-2.3773051500320435 +sweet-potato,NLD,-0.826541543006897 sweet-potato,NOR,-1.6521366238594055 sweet-potato,NPL,-2.2190775871276855 sweet-potato,NZL,-7.5 -sweet-potato,OMN,-3.8838921785354614 +sweet-potato,OMN,-4.5269447565078735 sweet-potato,PAK,0.14754696190357208 sweet-potato,PAN,7.5 -sweet-potato,PER,-2.048309326171875 +sweet-potato,PER,-1.970850944519043 sweet-potato,PHL,-1.2197287678718567 sweet-potato,PNG,-3.114119052886963 sweet-potato,POL,-0.6363043189048767 @@ -5206,8 +5206,8 @@ sweet-potato,PSE,-3.9317547380924225 sweet-potato,ROU,-5.1231852769851685 sweet-potato,RUS,-3.046185255050659 sweet-potato,RWA,-0.5826290249824524 -sweet-potato,SAU,-0.7435782849788666 -sweet-potato,SDN,-2.254224419593811 +sweet-potato,SAU,-0.6280774176120758 +sweet-potato,SDN,-2.371124505996704 sweet-potato,SEN,-7.5 sweet-potato,SLB,-1.7353721261024475 sweet-potato,SLE,-2.0892189741134644 @@ -5228,122 +5228,122 @@ sweet-potato,TJK,-2.6991398334503174 sweet-potato,TKM,-2.3853678703308105 sweet-potato,TLS,-1.1626556515693665 sweet-potato,TTO,-4.361142158508301 -sweet-potato,TUN,-2.72883403301239 -sweet-potato,TUR,-0.25368794798851013 +sweet-potato,TUN,-2.550648331642151 +sweet-potato,TUR,-0.11139227449893951 sweet-potato,TWN,-0.7262731194496155 sweet-potato,TZA,0.13157109916210175 sweet-potato,UGA,-3.3281540870666504 -sweet-potato,UKR,-2.610581398010254 +sweet-potato,UKR,-2.4937386512756348 sweet-potato,URY,-3.501429319381714 -sweet-potato,USA,-4.9965057373046875 +sweet-potato,USA,-4.966672897338867 sweet-potato,UZB,-1.8442890644073486 sweet-potato,VEN,-1.4053963422775269 sweet-potato,VNM,-2.4394280910491943 -sweet-potato,YEM,-1.9331015944480896 -sweet-potato,ZAF,1.448300540447235 +sweet-potato,YEM,-1.5333725810050964 +sweet-potato,ZAF,1.6726718544960022 sweet-potato,ZMB,-0.3304411768913269 -sweet-potato,ZWE,-1.2006644010543823 +sweet-potato,ZWE,-1.200044572353363 tea,AGO,7.5 tea,ALB,-7.5 tea,ARG,-0.0 tea,AZE,-3.75 -tea,BDI,1.8614802956581116 -tea,BGD,3.2185400128364563 -tea,BLZ,2.5415879487991333 +tea,BDI,1.8891865015029907 +tea,BGD,3.2487334609031677 +tea,BLZ,2.5444761514663696 tea,BOL,7.5 -tea,BRA,1.178766131401062 +tea,BRA,1.1766932010650635 tea,BRN,-0.0 -tea,BTN,5.868613958358765 +tea,BTN,5.904338598251343 tea,CAF,-0.0 tea,CHN,-7.5 -tea,CMR,5.8768274784088135 -tea,COD,-2.9562110900878906 +tea,CMR,5.963584899902344 +tea,COD,-2.955566167831421 tea,COG,7.5 -tea,COL,3.6371939182281494 +tea,COL,3.6500219851732254 tea,ECU,-7.5 -tea,ETH,2.378664016723633 -tea,GAB,4.573431313037872 +tea,ETH,2.4312968254089355 +tea,GAB,4.596061050891876 tea,GEO,-7.5 tea,GNQ,7.5 -tea,GRC,-5.002845406532288 -tea,GTM,-2.64535391330719 +tea,GRC,-5.675933599472046 +tea,GTM,-2.6417568922042847 tea,GUF,-0.0 tea,GUY,-0.0 -tea,HND,-1.2765215039253235 +tea,HND,-1.259864091873169 tea,HRV,-0.0 -tea,IDN,1.4882097244262695 -tea,IND,5.4737160205841064 +tea,IDN,1.5326989889144897 +tea,IND,5.55724024772644 tea,IRN,-0.0 tea,JPN,-7.5 tea,KEN,-7.5 -tea,KHM,-0.7029614150524139 -tea,KOR,4.450802803039551 -tea,LAO,-0.20716995000839233 -tea,LKA,0.47010675072669983 -tea,MDG,-2.767202615737915 -tea,MEX,-2.241687297821045 -tea,MMR,-1.9912601709365845 -tea,MNE,-4.866828799247742 -tea,MOZ,-2.0700316429138184 -tea,MUS,-0.4847933053970337 -tea,MWI,5.536350727081299 -tea,MYS,0.6632167100906372 +tea,KHM,-0.6802046895027161 +tea,KOR,4.442820072174072 +tea,LAO,-0.1794995367527008 +tea,LKA,0.5032947510480881 +tea,MDG,-2.764731287956238 +tea,MEX,-2.2342718839645386 +tea,MMR,-1.981156826019287 +tea,MNE,-4.862995624542236 +tea,MOZ,-2.060798406600952 +tea,MUS,-0.4677739143371582 +tea,MWI,5.5724804401397705 +tea,MYS,0.6993071436882019 tea,NGA,-0.0 -tea,NPL,4.823797941207886 +tea,NPL,4.846561789512634 tea,PAN,-0.0 -tea,PER,0.6862542629241943 -tea,PNG,2.1696730852127075 -tea,PRY,0.033782316371798515 -tea,RUS,-4.960223197937012 -tea,RWA,2.035746932029724 -tea,SLV,-1.4023597836494446 +tea,PER,0.32681748270988464 +tea,PNG,2.2211955785751343 +tea,PRY,0.06303172372281551 +tea,RUS,-4.93283224105835 +tea,RWA,2.0669684410095215 +tea,SLV,-1.3869080543518066 tea,SSD,-7.5 tea,SUR,-0.0 tea,SWZ,-0.0 -tea,THA,-1.5309996008872986 -tea,TLS,3.17325496673584 -tea,TUR,-4.421154022216797 -tea,TWN,-0.3423171937465668 -tea,TZA,1.541098713874817 -tea,UGA,6.9501051902771 -tea,URY,2.3135640621185303 +tea,THA,-1.5137776732444763 +tea,TLS,3.1824600100517273 +tea,TUR,-4.391909599304199 +tea,TWN,-0.31592293083667755 +tea,TZA,1.6239618062973022 +tea,UGA,6.995952129364014 +tea,URY,2.3634421825408936 tea,VEN,-0.0 -tea,VNM,4.110390901565552 -tea,ZAF,-1.5871448516845703 -tea,ZMB,-1.407898187637329 -tea,ZWE,1.9223720729351044 +tea,VNM,4.187098979949951 +tea,ZAF,-1.5742850303649902 +tea,ZMB,-1.392243504524231 +tea,ZWE,1.9525763988494873 tomato,AFG,-7.5 tomato,AGO,-7.5 tomato,ALB,-7.5 -tomato,ARE,-3.6632336685433984 +tomato,ARE,-3.6287593841552734 tomato,ARG,-7.5 -tomato,ARM,-1.5786635279655457 +tomato,ARM,-1.5352747440338135 tomato,ATG,7.5 tomato,AUS,-7.5 tomato,AUT,-7.5 -tomato,AZE,-7.174182176589966 +tomato,AZE,-7.0152270793914795 tomato,BDI,-7.5 tomato,BEL,0.0 -tomato,BEN,-1.657129943370819 +tomato,BEN,-1.6935946345329285 tomato,BFA,-7.5 -tomato,BGD,-0.0298149436712265 -tomato,BGR,-7.211445331573486 -tomato,BHS,-1.2161182165145874 -tomato,BIH,-2.666550557129085 +tomato,BGD,-1.1337255835533142 +tomato,BGR,-7.187605619430542 +tomato,BHS,-1.1332463026046753 +tomato,BIH,-2.6654205382801592 tomato,BLR,-7.5 -tomato,BLZ,-6.939023733139038 -tomato,BOL,-0.6743876934051514 +tomato,BLZ,-6.924262523651123 +tomato,BOL,-0.6569191813468933 tomato,BRA,-7.5 tomato,BRB,-7.5 tomato,BRN,-7.5 -tomato,BTN,0.30472099781036377 +tomato,BTN,0.26193559169769287 tomato,BWA,-7.5 tomato,CAF,-7.5 -tomato,CAN,-4.29317569732666 +tomato,CAN,-4.343989849090576 tomato,CHE,-7.5 tomato,CHL,-7.5 tomato,CHN,-7.5 -tomato,CIV,-3.7645280361175537 +tomato,CIV,-3.7544134855270386 tomato,CMR,-7.5 tomato,COD,-7.5 tomato,COG,-7.5 @@ -5357,21 +5357,21 @@ tomato,CZE,-7.5 tomato,DEU,-7.5 tomato,DJI,-7.5 tomato,DNK,0.0 -tomato,DOM,0.7082383632659912 +tomato,DOM,0.680347204208374 tomato,DZA,-7.5 -tomato,ECU,-7.092607259750366 -tomato,EGY,-1.1159127950668335 +tomato,ECU,-7.081485986709595 +tomato,EGY,-1.9851488769054413 tomato,ERI,7.5 -tomato,ESP,2.684962749481201 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tomato,LAO,-7.5 tomato,LBN,-7.5 tomato,LBR,-7.5 tomato,LBY,-7.5 -tomato,LKA,-3.2676563262939453 -tomato,LSO,1.4017441272735596 +tomato,LKA,-3.2532373666763306 +tomato,LSO,1.4362471103668213 tomato,LTU,-7.5 tomato,LUX,7.5 -tomato,LVA,-6.908875465393066 -tomato,MAR,0.15311138331890106 -tomato,MDA,1.9491099119186401 +tomato,LVA,-6.88941216468811 +tomato,MAR,0.7779547572135925 +tomato,MDA,1.7578262686729431 tomato,MDG,-7.5 tomato,MEX,-7.5 -tomato,MKD,-5.010006308555603 -tomato,MLI,-6.802495956420898 +tomato,MKD,-4.894958853721619 +tomato,MLI,-6.787128448486328 tomato,MLT,-7.5 -tomato,MMR,-5.847329616546631 +tomato,MMR,-5.810143709182739 tomato,MNE,-7.5 tomato,MNG,-7.5 tomato,MOZ,-7.5 tomato,MUS,-7.5 -tomato,MWI,-5.255628824234009 +tomato,MWI,-5.29223895072937 tomato,MYS,0.0 tomato,NAM,-7.5 -tomato,NER,-1.893690049648285 +tomato,NER,-3.903506875038147 tomato,NGA,-7.5 tomato,NIC,-7.5 tomato,NLD,0.0 tomato,NOR,7.5 tomato,NPL,-7.5 -tomato,NZL,-5.06737756729126 -tomato,OMN,-7.27644419670105 -tomato,PAK,-1.3342511653900146 +tomato,NZL,-5.01366913318634 +tomato,OMN,-7.245186805725098 +tomato,PAK,-1.3270916938781738 tomato,PAN,-7.5 tomato,PER,7.5 -tomato,PHL,-2.9531991481781006 +tomato,PHL,-2.9402718544006348 tomato,PNG,-7.5 tomato,POL,-7.5 tomato,PRI,-7.5 tomato,PRT,-7.5 tomato,PRY,-7.5 tomato,PSE,-7.5 -tomato,ROU,-6.55340051651001 +tomato,ROU,-6.535993814468384 tomato,RUS,-7.5 -tomato,RWA,-2.976225256919861 +tomato,RWA,-2.9683562517166138 tomato,SAU,-7.5 tomato,SDN,-7.5 -tomato,SEN,-2.8794060945510864 -tomato,SLE,-7.499494314193726 -tomato,SLV,-7.268247127532959 +tomato,SEN,-2.8606356382369995 +tomato,SLE,-7.495115518569946 +tomato,SLV,-7.2340898513793945 tomato,SOM,-7.5 -tomato,SRB,-1.5039362609386444 +tomato,SRB,-1.4625967741012573 tomato,SSD,-7.5 tomato,SUR,-7.5 tomato,SVK,-7.5 @@ -5459,592 +5459,592 @@ tomato,SWE,-7.5 tomato,SWZ,-7.5 tomato,SYR,-7.5 tomato,TCD,-7.5 -tomato,TGO,-0.035080552101135254 -tomato,THA,-4.046413898468018 +tomato,TGO,-0.024904191493988037 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watermelon,AUS,-7.5 -watermelon,AZE,5.439724445343018 +watermelon,AZE,5.560161590576172 watermelon,BDI,-0.0 watermelon,BEN,-0.0 watermelon,BFA,-0.0 watermelon,BGD,-0.0 watermelon,BGR,-0.0 watermelon,BHS,-0.0 -watermelon,BIH,-2.6876749992370605 -watermelon,BLZ,-2.2422980219125748 -watermelon,BOL,0.8754689693450928 -watermelon,BRA,3.3961633443832397 +watermelon,BIH,-2.596220016479492 +watermelon,BLZ,-2.3267299064900726 +watermelon,BOL,0.9752515554428101 +watermelon,BRA,3.366129994392395 watermelon,BRB,-0.0 watermelon,BTN,-0.0 -watermelon,BWA,-0.48990047723054886 +watermelon,BWA,-0.33143285661935806 watermelon,CAF,-0.0 -watermelon,CHL,-0.6111423969268799 -watermelon,CHN,-7.458738327026367 +watermelon,CHL,-0.36094510555267334 +watermelon,CHN,-7.322009086608887 watermelon,CIV,-0.0 -watermelon,CMR,-4.943110466003418 +watermelon,CMR,-4.894107341766357 watermelon,COD,-0.0 watermelon,COG,-0.0 -watermelon,COL,2.3931825160980225 +watermelon,COL,2.5705206394195557 watermelon,COM,-0.0 watermelon,CPV,-7.5 -watermelon,CRI,0.5879246294498444 -watermelon,CUB,-2.2151666283607483 -watermelon,CYP,-4.171285390853882 +watermelon,CRI,0.8863120973110199 +watermelon,CUB,-2.101251184940338 +watermelon,CYP,-3.8646140098571777 watermelon,DOM,-7.5 -watermelon,DZA,-0.13445048034191132 -watermelon,ECU,-1.9608731865882874 -watermelon,EGY,-1.3446671962738037 +watermelon,DZA,0.15978077054023743 +watermelon,ECU,-1.8469477891921997 +watermelon,EGY,-2.8423166275024414 watermelon,ERI,-0.0 watermelon,ESP,-7.5 watermelon,ETH,-0.0 watermelon,FJI,-7.5 -watermelon,FRA,0.5448762327432632 +watermelon,FRA,0.7472826540470123 watermelon,GAB,-0.0 -watermelon,GEO,-1.0129250884056091 +watermelon,GEO,-0.9831418097019196 watermelon,GHA,-0.0 -watermelon,GIN,-5.15057110786438 +watermelon,GIN,-6.67816424369812 watermelon,GMB,-0.0 watermelon,GNB,-0.0 -watermelon,GRC,-0.9239271879196167 -watermelon,GTM,0.23168183863162994 +watermelon,GRC,-0.45973944664001465 +watermelon,GTM,0.40609948337078094 watermelon,GUF,-0.0 watermelon,GUY,-7.5 watermelon,HND,-7.5 -watermelon,HRV,-2.909970998764038 +watermelon,HRV,-2.8359405994415283 watermelon,HTI,-0.0 -watermelon,IDN,1.7484290599822998 -watermelon,IND,-2.253192663192749 -watermelon,IRN,4.544487476348877 +watermelon,IDN,1.835102617740631 +watermelon,IND,-2.185990810394287 +watermelon,IRN,4.646559238433838 watermelon,IRQ,-0.0 -watermelon,ISR,-0.5167399346828461 -watermelon,ITA,2.693014994263649 +watermelon,ISR,-0.354593001306057 +watermelon,ITA,2.577204279601574 watermelon,JAM,-7.5 -watermelon,JOR,-4.590541362762451 +watermelon,JOR,-4.3271870613098145 watermelon,JPN,-7.5 -watermelon,KEN,2.8220441341400146 -watermelon,KGZ,-4.020353317260742 +watermelon,KEN,3.000869393348694 +watermelon,KGZ,-3.9786298274993896 watermelon,KHM,-0.0 -watermelon,LAO,-2.5132986903190613 +watermelon,LAO,-2.4499552845954895 watermelon,LBN,-7.5 watermelon,LBR,-0.0 -watermelon,LBY,1.2341705560684204 +watermelon,LBY,1.4386487007141113 watermelon,LKA,-0.0 watermelon,LSO,-0.0 -watermelon,MAR,-1.4122843742370605 +watermelon,MAR,-2.459293842315674 watermelon,MDG,-0.0 -watermelon,MEX,-0.0016701103886589408 -watermelon,MKD,4.540061712265015 -watermelon,MLI,3.171576738357544 -watermelon,MLT,-6.9400315284729 +watermelon,MEX,0.1361916959285736 +watermelon,MKD,4.739121198654175 +watermelon,MLI,3.428459405899048 +watermelon,MLT,-7.432108163833618 watermelon,MMR,-0.0 -watermelon,MNE,-0.18498051166534424 +watermelon,MNE,0.058615148067474365 watermelon,MOZ,-0.0 -watermelon,MRT,-4.583128929138184 +watermelon,MRT,-4.526674270629883 watermelon,MUS,-0.0 -watermelon,MWI,-0.4575558379292488 -watermelon,MYS,2.9594984650611877 -watermelon,NAM,-2.1203548908233643 +watermelon,MWI,-0.3334113359451294 +watermelon,MYS,3.0703503638505936 +watermelon,NAM,-2.002815008163452 watermelon,NER,-7.5 watermelon,NGA,-0.0 watermelon,NIC,-0.0 watermelon,NPL,-0.0 watermelon,OMN,7.5 -watermelon,PAK,1.022446632385254 -watermelon,PER,7.286360740661621 -watermelon,PHL,0.3446003496646881 +watermelon,PAK,1.1098885536193848 +watermelon,PER,6.451971054077148 +watermelon,PHL,0.4154865890741348 watermelon,PNG,-0.0 -watermelon,PRI,-4.576330065727234 -watermelon,PRT,-5.66956901550293 -watermelon,PRY,2.7808659076690674 +watermelon,PRI,-4.4076584577560425 +watermelon,PRT,-5.530441045761108 +watermelon,PRY,2.887900710105896 watermelon,PSE,-7.5 watermelon,RUS,-0.0 watermelon,RWA,-0.0 -watermelon,SAU,-0.38278186321258545 -watermelon,SDN,-2.5563907623291016 -watermelon,SEN,2.838209867477417 +watermelon,SAU,-0.17156219482421875 +watermelon,SDN,-2.510256052017212 +watermelon,SEN,2.959707260131836 watermelon,SLE,-0.0 -watermelon,SLV,4.2482099533081055 -watermelon,SOM,-2.4614200592041016 +watermelon,SLV,4.485765695571899 +watermelon,SOM,-2.3534910678863525 watermelon,SSD,-0.0 -watermelon,SUR,-6.213452339172363 -watermelon,SVN,-1.8974599242210388 +watermelon,SUR,-6.098660230636597 +watermelon,SVN,-1.4165443778038025 watermelon,SWZ,-0.0 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-white-potato,ARG,-4.892858505249023 -white-potato,ARM,-1.324949562549591 +wheat,YEM,-0.5378095507621765 +wheat,ZAF,-0.34150031208992004 +wheat,ZMB,0.2244870662689209 +wheat,ZWE,2.1696196794509888 +white-potato,AFG,-4.273266077041626 +white-potato,AGO,-6.26181697845459 +white-potato,ALB,-5.259927988052368 +white-potato,ARE,-5.20692503452301 +white-potato,ARG,-4.9685163497924805 +white-potato,ARM,-1.3756675124168396 white-potato,ATG,-7.5 white-potato,AUS,7.5 -white-potato,AUT,-0.780416913330555 -white-potato,AZE,-2.3607194423675537 -white-potato,BDI,-1.583346426486969 -white-potato,BEL,1.345667839050293 +white-potato,AUT,-0.8707465827465057 +white-potato,AZE,-2.302988827228546 +white-potato,BDI,-1.604629635810852 +white-potato,BEL,1.219574123620987 white-potato,BEN,0.0 -white-potato,BFA,-6.657420635223389 -white-potato,BGD,0.6834058687090874 -white-potato,BGR,0.01282954216003418 -white-potato,BHS,-7.187550783157349 -white-potato,BIH,-1.4009221196174622 -white-potato,BLR,-0.5376383662223816 -white-potato,BLZ,-6.015321969985962 -white-potato,BOL,-0.8154157996177673 -white-potato,BRA,-6.683359622955322 +white-potato,BFA,-6.6656270027160645 +white-potato,BGD,0.044055698439478874 +white-potato,BGR,-0.04688620567321777 +white-potato,BHS,-7.19072961807251 +white-potato,BIH,-1.4321695566177368 +white-potato,BLR,-0.6508108377456665 +white-potato,BLZ,-6.037864923477173 +white-potato,BOL,-0.8289272785186768 +white-potato,BRA,-6.730381965637207 white-potato,BRB,-7.5 white-potato,BRN,-7.5 -white-potato,BTN,-2.830598473548889 +white-potato,BTN,-2.918095111846924 white-potato,BWA,-7.5 white-potato,CAF,-7.5 -white-potato,CAN,-2.336838722229004 +white-potato,CAN,-2.4581068754196167 white-potato,CHE,-7.5 -white-potato,CHL,-5.076667785644531 +white-potato,CHL,-5.123201370239258 white-potato,CHN,-0.16349898278713226 -white-potato,CIV,-7.068480491638184 -white-potato,CMR,-6.015861749649048 -white-potato,COD,-6.667510986328125 -white-potato,COG,-6.153130292892456 -white-potato,COL,-2.7065480947494507 -white-potato,COM,-7.182870626449585 -white-potato,CPV,-6.651533603668213 +white-potato,CIV,-7.070825576782227 +white-potato,CMR,-6.032779932022095 +white-potato,COD,-6.675407409667969 +white-potato,COG,-6.168162107467651 +white-potato,COL,-2.7693240642547607 +white-potato,COM,-7.185315847396851 +white-potato,CPV,-7.4346349239349365 white-potato,CRI,-7.5 -white-potato,CUB,-4.543330192565918 -white-potato,CYP,-4.8008973598480225 -white-potato,CZE,-0.1767502874135971 -white-potato,DEU,0.3590337112545967 -white-potato,DNK,-2.760642886161804 +white-potato,CUB,-4.591577529907227 +white-potato,CYP,-4.788880228996277 +white-potato,CZE,-0.26966798305511475 +white-potato,DEU,0.22789596021175385 +white-potato,DNK,-2.885556697845459 white-potato,DOM,-7.5 -white-potato,DZA,-5.447375059127808 -white-potato,ECU,-1.6488671898841858 -white-potato,EGY,-2.450685739517212 -white-potato,ERI,-7.137639999389648 -white-potato,ESP,-5.260821104049683 -white-potato,EST,0.8071079850196838 -white-potato,ETH,0.5625659227371216 -white-potato,FIN,-0.6591880619525909 +white-potato,DZA,-5.21607518196106 +white-potato,ECU,-1.6933956146240234 +white-potato,EGY,-3.0571908950805664 +white-potato,ERI,-7.1389689445495605 +white-potato,ESP,-5.269536733627319 +white-potato,EST,0.725942850112915 +white-potato,ETH,0.5264977216720581 +white-potato,FIN,-0.7081965506076813 white-potato,FRA,-7.5 -white-potato,GAB,-7.02944278717041 -white-potato,GBR,-3.462641716003418 -white-potato,GEO,-0.5480959415435791 -white-potato,GHA,-7.048776149749756 -white-potato,GIN,-6.113731145858765 +white-potato,GAB,-7.032233715057373 +white-potato,GBR,-3.5475672483444214 +white-potato,GEO,-0.576615184545517 +white-potato,GHA,-7.051360130310059 +white-potato,GIN,-6.131713151931763 white-potato,GMB,0.0 -white-potato,GNB,-6.739335060119629 -white-potato,GNQ,-6.760073184967041 +white-potato,GNB,-6.7464892864227295 +white-potato,GNQ,-6.766905069351196 white-potato,GRC,-7.5 -white-potato,GRD,-6.830006122589111 -white-potato,GTM,-3.0130324363708496 +white-potato,GRD,-6.835678577423096 +white-potato,GTM,-3.0884318351745605 white-potato,GUF,-7.5 white-potato,GUY,3.75 -white-potato,HND,-7.193747520446777 -white-potato,HRV,-3.535812556743622 -white-potato,HTI,-6.67333722114563 -white-potato,HUN,-1.615894422866404 -white-potato,IDN,-5.755913972854614 -white-potato,IND,-2.791849732398987 -white-potato,IRL,-0.08122962340712547 -white-potato,IRN,-0.15691087394952774 -white-potato,IRQ,-3.7558659315109253 +white-potato,HND,-7.221832752227783 +white-potato,HRV,-2.953004777431488 +white-potato,HTI,-6.683378219604492 +white-potato,HUN,-3.453266143798828 +white-potato,IDN,-5.759391784667969 +white-potato,IND,-2.885191798210144 +white-potato,IRL,-0.1925703026354313 +white-potato,IRN,-0.23729869723320007 +white-potato,IRQ,-3.8475557565689087 white-potato,ISR,-7.5 white-potato,ITA,-7.5 -white-potato,JAM,-6.277691841125488 -white-potato,JOR,-7.282428741455078 +white-potato,JAM,-6.339934587478638 +white-potato,JOR,-7.323810815811157 white-potato,JPN,-7.5 -white-potato,KAZ,-0.5737046003341675 -white-potato,KEN,-1.2727055549621582 -white-potato,KGZ,-1.7841445803642273 -white-potato,KHM,-6.010222434997559 +white-potato,KAZ,-0.5695942938327789 +white-potato,KEN,-1.2899346351623535 +white-potato,KGZ,-1.7854214906692505 +white-potato,KHM,-6.010950565338135 white-potato,KOR,-3.7122304439544678 -white-potato,LAO,1.9184892177581787 -white-potato,LBN,-6.99291467666626 -white-potato,LBR,-5.348794460296631 -white-potato,LBY,-3.6757400035858154 -white-potato,LKA,-7.252150774002075 -white-potato,LSO,-2.7024563550949097 -white-potato,LTU,-0.26730214804410934 -white-potato,LUX,-3.781416177749634 -white-potato,LVA,1.099543571472168 -white-potato,MAR,-2.1044013500213623 -white-potato,MDA,0.612177312374115 -white-potato,MDG,-6.309719562530518 -white-potato,MEX,-6.141382694244385 -white-potato,MKD,-1.4117391109466553 +white-potato,LAO,1.9174374341964722 +white-potato,LBN,-7.405105829238892 +white-potato,LBR,-5.376176357269287 +white-potato,LBY,-2.770139217376709 +white-potato,LKA,-7.288463354110718 +white-potato,LSO,-2.7611703872680664 +white-potato,LTU,-0.317354753613472 +white-potato,LUX,-3.8618324995040894 +white-potato,LVA,1.0376612544059753 +white-potato,MAR,-2.173063278198242 +white-potato,MDA,0.11083411797881126 +white-potato,MDG,-6.322059154510498 +white-potato,MEX,-6.221388578414917 +white-potato,MKD,-1.3861267268657684 white-potato,MLI,-7.5 white-potato,MLT,-7.5 -white-potato,MMR,-4.4864342212677 -white-potato,MNE,-4.132070183753967 +white-potato,MMR,-4.488037109375 +white-potato,MNE,-4.179803848266602 white-potato,MNG,-1.2369403839111328 white-potato,MOZ,-7.5 white-potato,MRT,-7.5 white-potato,MUS,-7.5 -white-potato,MWI,2.372127890586853 -white-potato,MYS,1.9422821998596191 -white-potato,NAM,-5.190667152404785 +white-potato,MWI,2.3443410396575928 +white-potato,MYS,1.9412293434143066 +white-potato,NAM,-5.21712589263916 white-potato,NER,-7.5 -white-potato,NGA,-6.870185852050781 +white-potato,NGA,-6.875276565551758 white-potato,NIC,-7.5 -white-potato,NLD,-0.6496542692184448 -white-potato,NOR,-5.8460187911987305 -white-potato,NPL,-1.3680104613304138 +white-potato,NLD,-0.7639261782169342 +white-potato,NOR,-5.89995265007019 +white-potato,NPL,-1.4009449481964111 white-potato,NZL,-0.7484760284423828 white-potato,OMN,-7.5 white-potato,PAK,-0.0 white-potato,PAN,-7.5 -white-potato,PER,-2.5240437984466553 +white-potato,PER,-2.9024757146835327 white-potato,PHL,-7.5 white-potato,PNG,-7.5 -white-potato,POL,2.1020238399505615 +white-potato,POL,2.020090401172638 white-potato,PRI,-7.5 -white-potato,PRT,-2.033027172088623 -white-potato,PRY,-2.867590069770813 +white-potato,PRT,-2.1010612845420837 +white-potato,PRY,-2.885186791419983 white-potato,PSE,-7.5 -white-potato,ROU,-2.192002058029175 -white-potato,RUS,-0.3084056079387665 -white-potato,RWA,-1.3822731375694275 +white-potato,ROU,-2.2432901859283447 +white-potato,RUS,-0.31007978320121765 +white-potato,RWA,-1.3943694829940796 white-potato,SAU,-7.5 -white-potato,SDN,-5.008236646652222 -white-potato,SEN,-5.62498927116394 +white-potato,SDN,-5.040076732635498 +white-potato,SEN,-5.7163496017456055 white-potato,SLB,-4.641535997390747 -white-potato,SLE,0.7965755462646484 -white-potato,SLV,0.9726858139038086 -white-potato,SOM,-5.723247051239014 -white-potato,SRB,1.4379744827747345 +white-potato,SLE,0.7869491577148438 +white-potato,SLV,0.929577112197876 +white-potato,SOM,-5.743017196655273 +white-potato,SRB,1.3489886224269867 white-potato,SSD,-7.5 white-potato,SUR,-7.5 -white-potato,SVK,-1.6193366423249245 -white-potato,SVN,-2.6869579553604126 -white-potato,SWE,-4.817352771759033 -white-potato,SWZ,-6.997324466705322 -white-potato,SYR,-3.6495532989501953 -white-potato,TCD,-6.66522479057312 -white-potato,TGO,-6.855196714401245 -white-potato,THA,-2.274385929107666 -white-potato,TJK,-1.6017606258392334 -white-potato,TKM,-5.116387844085693 +white-potato,SVK,-1.7882756888866425 +white-potato,SVN,-2.76405930519104 +white-potato,SWE,-4.901193618774414 +white-potato,SWZ,-7.000478506088257 +white-potato,SYR,-3.70100998878479 +white-potato,TCD,-6.6719114780426025 +white-potato,TGO,-6.860534191131592 +white-potato,THA,-2.2763779163360596 +white-potato,TJK,-1.6369490027427673 +white-potato,TKM,-5.146193981170654 white-potato,TLS,-0.47124533355236053 -white-potato,TTO,-6.704572677612305 -white-potato,TUN,-1.308105707168579 -white-potato,TUR,-1.3780488967895508 -white-potato,TWN,-2.6755971908569336 -white-potato,TZA,-0.15896368026733398 -white-potato,UGA,-6.633254766464233 -white-potato,UKR,0.5078345239162445 +white-potato,TTO,-6.71565318107605 +white-potato,TUN,-1.3302057683467865 +white-potato,TUR,-1.465464472770691 +white-potato,TWN,-2.6782476902008057 +white-potato,TZA,-0.17422252893447876 +white-potato,UGA,-6.641678094863892 +white-potato,UKR,-0.06291117519140244 white-potato,URY,-7.5 -white-potato,USA,0.7869330644607544 -white-potato,UZB,-3.1113035678863525 +white-potato,USA,0.5926850438117981 +white-potato,UZB,-3.155489206314087 white-potato,VEN,-7.5 -white-potato,VNM,-4.910987377166748 -white-potato,YEM,-5.8191752433776855 -white-potato,ZAF,-3.93700909614563 +white-potato,VNM,-4.912402629852295 +white-potato,YEM,-5.7462873458862305 +white-potato,ZAF,-3.9312623739242554 white-potato,ZMB,-7.5 white-potato,ZWE,-7.5 yam,AFG,-0.0 @@ -6060,8 +6060,8 @@ yam,BOL,-5.210255146026611 yam,BRA,-2.879700779914856 yam,BRB,-7.5 yam,BRN,-5.240689039230347 -yam,BTN,-2.9088377952575684 -yam,BWA,-3.6050477027893066 +yam,BTN,-3.0268924236297607 +yam,BWA,-3.6056050062179565 yam,CAF,-4.071576118469238 yam,CHN,-0.7571600675582886 yam,CIV,-0.7280837893486023 @@ -6072,9 +6072,9 @@ yam,COL,-1.327725887298584 yam,COM,-4.371114492416382 yam,CRI,-7.5 yam,CUB,-2.746282696723938 -yam,DOM,-1.9059787392616272 +yam,DOM,-1.9820622205734253 yam,ECU,-5.174973964691162 -yam,EGY,0.33129335939884186 +yam,EGY,-0.55497807264328 yam,ERI,-5.6153175830841064 yam,ETH,-1.8165869116783142 yam,FJI,-7.5 @@ -6101,12 +6101,12 @@ yam,JPN,-7.5 yam,KEN,-1.4882037043571472 yam,KHM,-2.6322381496429443 yam,LAO,0.9493604302406311 -yam,LBN,-4.759747624397278 +yam,LBN,-5.183978080749512 yam,LBR,-3.783653497695923 yam,LKA,-3.0860074758529663 yam,LSO,-0.0 yam,MDG,-4.15936541557312 -yam,MEX,2.388148307800293 +yam,MEX,2.3334678411483765 yam,MLI,-5.260771751403809 yam,MMR,-0.9187805652618408 yam,MNE,-0.0 @@ -6122,7 +6122,7 @@ yam,NIC,2.688100576400757 yam,NPL,-5.065113067626953 yam,PAK,-0.0 yam,PAN,-3.2264866828918457 -yam,PER,-3.998135209083557 +yam,PER,-4.394765138626099 yam,PHL,-1.6469984650611877 yam,PNG,-3.0244327783584595 yam,PRI,-7.5 diff --git a/data/curated/calibration/gbd-anchored/deviation_penalty.yaml b/data/curated/calibration/gbd-anchored/deviation_penalty.yaml index 1f0f9e87..052072fb 100644 --- a/data/curated/calibration/gbd-anchored/deviation_penalty.yaml +++ b/data/curated/calibration/gbd-anchored/deviation_penalty.yaml @@ -17,6 +17,6 @@ l1_costs: cropland: 1.6191595998981074 grassland: 0.24020086409748675 feed: 0.05576680765925167 -iterations: 4 +iterations: 0 converged: true -final_residual_log_inf: 0.010390503539944175 +final_residual_log_inf: 0.01785345275977553 diff --git a/data/curated/calibration/gbd-anchored/exogenous_feed.csv b/data/curated/calibration/gbd-anchored/exogenous_feed.csv index 6d43d8d1..4abac429 100644 --- a/data/curated/calibration/gbd-anchored/exogenous_feed.csv +++ b/data/curated/calibration/gbd-anchored/exogenous_feed.csv @@ -63,7 +63,7 @@ MMR,0.0,0.340268,0.0 MNE,0.0,0.0,0.002311 MNG,0.0,0.0,15.401712 MRT,0.0,0.0,2.102427 -MYS,0.955496,0.0,0.234748 +MYS,0.818989,0.136506,0.234748 NAM,0.0,0.0,0.700566 NER,0.0,0.0,1.710052 NIC,0.0,0.0,1.29114 diff --git a/data/curated/calibration/gbd-anchored/exogenous_forage.csv b/data/curated/calibration/gbd-anchored/exogenous_forage.csv index b6dfc491..6080df91 100644 --- a/data/curated/calibration/gbd-anchored/exogenous_forage.csv +++ b/data/curated/calibration/gbd-anchored/exogenous_forage.csv @@ -76,7 +76,7 @@ HRV,0.0 HTI,0.0 HUN,0.0 IDN,0.0 -IND,150.561417 +IND,150.546021 IRL,0.0 IRN,0.0 IRQ,0.0 @@ -157,7 +157,7 @@ TCD,0.0 TGO,0.0 THA,0.0 TJK,3.664683 -TKM,0.932468 +TKM,0.928371 TLS,0.0 TTO,0.0 TUN,0.0 diff --git a/data/curated/calibration/gbd-anchored/food_demand.csv b/data/curated/calibration/gbd-anchored/food_demand.csv index 8dab2a91..70cf7085 100644 --- a/data/curated/calibration/gbd-anchored/food_demand.csv +++ b/data/curated/calibration/gbd-anchored/food_demand.csv @@ -51,7 +51,7 @@ sorghum,1.0,0.0,9.408891627230332 soy,1.0,0.0,3.8865096387889935 soybean-oil,0.9218980539600212,2.327623707828934,29.80237786440648 sugar,1.0,0.0,154.03780619380996 -sunflower-oil,0.9843609927598227,0.20598383838660084,13.171158195861608 +sunflower-oil,0.9843610040646036,0.2059836894895426,13.171158195861608 sunflower-seed,1.0,0.0,1.9189327573915307 sweet-potato,1.0,0.0,55.038199765389436 tea-dried,0.9840023658971014,0.10676424254961603,6.673751997507679 diff --git a/data/curated/calibration/gbd-anchored/food_waste.yaml b/data/curated/calibration/gbd-anchored/food_waste.yaml index 99668cf0..b431cba6 100644 --- a/data/curated/calibration/gbd-anchored/food_waste.yaml +++ b/data/curated/calibration/gbd-anchored/food_waste.yaml @@ -16,8 +16,8 @@ fruits: oil: baseline_consumption_mt: 101.94318941038546 baseline_negative_slack_mt: 0.34906473755836487 - baseline_positive_slack_mt: 51.10373346695246 - waste_retention_multiplier: 1.9915244288011165 + baseline_positive_slack_mt: 51.10373316892924 + waste_retention_multiplier: 1.9915244172063191 starchy_vegetable: baseline_consumption_mt: 548.2428959218498 baseline_negative_slack_mt: 0.0 diff --git a/data/curated/calibration/gbd-anchored/grassland_cost.csv b/data/curated/calibration/gbd-anchored/grassland_cost.csv index 39885ae2..53345efc 100644 --- a/data/curated/calibration/gbd-anchored/grassland_cost.csv +++ b/data/curated/calibration/gbd-anchored/grassland_cost.csv @@ -1,124 +1,124 @@ country,correction_bnusd_per_mha AFG,-4.0427408296017653e-10 AGO,-0.38177555799484253 -ALB,-0.20878468453884125 +ALB,-0.02966886665672064 ARE,-0.0 -ARG,-0.1856212317943573 +ARG,-0.18560367077589035 ARM,-0.0 ATG,-0.0 -AUS,-0.04442281275987625 +AUS,-0.033983536064624786 AUT,9.24111176470177e-10 -AZE,0.7331931293010712 +AZE,0.7445247769355774 BDI,-0.49132993817329407 BEL,0.08877222612500191 -BEN,2.2527434825897217 -BFA,1.0416541993618011 -BGD,-0.0 -BGR,-0.30829504132270813 +BEN,2.286935567855835 +BFA,1.0576006472110748 +BGD,-2.577699165229319e-11 +BGR,-0.30761292576789856 BHS,-0.03126266412436962 -BIH,-0.4584890455007553 +BIH,-0.4582430422306061 BLR,-0.01833034586161375 BLZ,-0.7399452030658722 BOL,-0.6221835613250732 BRA,-0.5207413733005524 -BRB,0.6817097067832947 +BRB,0.6931010782718658 BRN,-3.437714735099462e-08 -BTN,-0.3201640695333481 +BTN,-0.31162402033805847 BWA,-0.0 -CAF,-1.7237690386195936e-08 -CAN,-0.21300436556339264 -CHE,-0.12198027223348618 +CAF,-0.44557350873947144 +CAN,-0.21225376427173615 +CHE,-0.11700139567255974 CHL,-0.03965021949261427 -CHN,-4.022972177608608e-09 -CIV,-0.5443657338619232 -CMR,2.140450656414032 +CHN,-3.6901237621123073e-09 +CIV,-0.5425155460834503 +CMR,2.171208679676056 COD,-0.7532423138618469 COG,-0.8099571466445923 COL,-0.6204277873039246 -COM,1.1216293573379517 +COM,1.1377061009407043 CPV,-0.0 CRI,-0.04483989253640175 CUB,-0.17085150629281998 -CYP,0.2356496825814247 +CYP,0.2389899417757988 CZE,-0.2163601964712143 DEU,-0.2619142234325409 DJI,-0.0 -DNK,1.4884825348854065 +DNK,1.5109124779701233 DOM,-0.4018055945634842 -DZA,-0.0011151906801387668 +DZA,-0.0004372130206320435 ECU,-0.19687405228614807 EGY,-0.0 ERI,-0.04815981723368168 -ESP,-0.03535165823996067 +ESP,-0.04996956139802933 EST,-0.26680906116962433 -ETH,-2.0605006589846653e-09 -FIN,1.267688274383545 -FJI,-0.8799494802951813 +ETH,-4.5401093995423025e-09 +FIN,1.2865681052207947 +FJI,-0.8793256282806396 FRA,-2.9409451540729492e-09 GAB,-0.8222148716449738 -GBR,-0.028139030560851097 +GBR,-0.024770827032625675 GEO,-0.20096751302480698 GHA,-0.5431598126888275 GIN,-0.37204913794994354 -GMB,0.9210774004459381 -GNB,0.8557053804397583 +GMB,0.9349673092365265 +GNB,0.8686172068119049 GNQ,-0.7101413011550903 -GRC,-0.1523909792304039 -GRD,0.47368717193603516 +GRC,-0.15078746527433395 +GRD,0.4827237129211426 GTM,-2.610952076764761e-08 GUF,-0.9834447503089905 GUY,-1.1032667756080627 HND,-0.6275273263454437 -HRV,-0.17099863290786743 +HRV,-0.1705218255519867 HTI,1.2459450360324809e-08 HUN,-0.4085644632577896 IDN,-0.562181144952774 -IND,1.2133966088294983 +IND,1.242975890636444 IRL,-1.1858245052565053e-08 IRN,-0.0 IRQ,-0.0 ISL,-0.09247573837637901 -ISR,0.5439601242542267 -ITA,0.4403725564479828 -JAM,2.761610507965088 +ISR,0.5517217814922333 +ITA,0.48305410146713257 +JAM,2.8084521293640137 JOR,-0.0 JPN,-0.4728243798017502 KAZ,-4.997817021346052e-10 KEN,-0.2179667055606842 KGZ,-1.2954760597594372e-09 KHM,-2.9558109737592986e-08 -KOR,2.4750083684921265 -LAO,4.666451669521621e-10 -LBN,0.39881160855293274 +KOR,2.509803295135498 +LAO,-0.3563753366470337 +LBN,0.40448087453842163 LBR,-0.7191254794597626 -LBY,2.6584022136399987e-10 -LKA,-0.1928924173116684 +LBY,3.914085722200866e-10 +LKA,-0.4923660457134247 LSO,-0.43739476799964905 LTU,-0.07347145304083824 -LUX,0.008767670951783657 +LUX,0.005697618238627911 LVA,-0.2936539202928543 MAR,-0.0 MDA,-0.3453979641199112 MDG,-0.6868391931056976 MEX,-0.0679156482219696 -MKD,-0.27600471675395966 +MKD,-0.28447867929935455 MLI,-0.037232923321425915 MLT,-0.0 -MMR,2.074207365512848 -MNE,-0.3422906696796417 -MNG,-0.038229407742619514 +MMR,2.0936436653137207 +MNE,-0.34057021141052246 +MNG,-0.03807085193693638 MOZ,-0.5160333812236786 -MRT,-0.033999696373939514 +MRT,-2.3844572150100163e-10 MUS,-3.315589225394433e-08 MWI,-3.3964567824895653e-09 -MYS,1.4313578766689261e-08 +MYS,-0.5090545117855072 NAM,-0.0047507211565971375 NER,-0.0 NGA,-0.14038769900798798 NIC,-0.5779949724674225 NLD,0.8321638405323029 -NOR,1.0499045252799988 -NPL,0.021747911348938942 +NOR,1.0656535029411316 +NPL,0.023577503859996796 NZL,-0.0 OMN,-0.0 PAK,-0.0 @@ -126,50 +126,50 @@ PAN,-0.6221974492073059 PER,-0.6955994665622711 PHL,-0.11315194144845009 PNG,-0.8056034743785858 -POL,-0.036101678386330605 +POL,-0.031089939177036285 PRI,-2.7517170764213006e-09 -PRT,-0.18238242715597153 +PRT,-0.18188903480768204 PRY,-0.6874429285526276 -PSE,0.5131678581237793 +PSE,0.5204852670431137 ROU,-0.4107300937175751 RUS,-0.0 RWA,1.4384679225543096e-08 SAU,-0.0 -SDN,-0.07489163428544998 -SEN,0.28525300323963165 +SDN,-0.0744633711874485 +SEN,0.28956005722284317 SLB,-2.5197355313366643e-10 SLE,-0.3628302663564682 -SLV,2.6754162311553955 +SLV,2.720273494720459 SOM,-0.05690718814730644 SRB,-0.2917206585407257 SSD,-0.2865627706050873 -STP,0.3985329568386078 +STP,0.40543466806411743 SUR,-0.13022971898317337 SVK,-0.4101209193468094 SVN,-0.27246034145355225 -SWE,1.1129473447799683 +SWE,1.1296730041503906 SWZ,-2.0683636137164285e-08 -SYR,0.10907374322414398 +SYR,0.11062177270650864 TCD,-0.08657535165548325 TGO,-0.30542606115341187 THA,-1.830725793183774e-08 -TJK,0.3043214976787567 +TJK,0.31027569621801376 TKM,-0.0 TLS,-0.22256753593683243 TTO,-3.4331349985095017e-09 -TUN,0.6830682754516602 -TUR,0.7784259021282196 -TWN,-0.4033449739217758 +TUN,0.6933020949363708 +TUR,0.7899524867534637 +TWN,-0.402083620429039 TZA,-0.40795499086380005 -UGA,-2.739427173992226e-08 -UKR,-0.007475577760487795 +UGA,-3.941181603295263e-08 +UKR,-4.68292205013654e-09 URY,-0.38374826312065125 -USA,-0.21028513461351395 +USA,-0.21033576130867004 UZB,-0.0 VEN,-0.340562105178833 -VNM,4.418777227401733 +VNM,4.430349588394165 VUT,-0.19533131271600723 YEM,-6.187674372881702e-09 -ZAF,-0.04584670439362526 +ZAF,-5.673877367939895e-09 ZMB,-0.39802390336990356 -ZWE,0.3660371880978346 +ZWE,0.3733277842402458 diff --git a/data/curated/calibration/gbd-anchored/multi_crop_cost.csv b/data/curated/calibration/gbd-anchored/multi_crop_cost.csv index 9ec86a0f..da7b1ffa 100644 --- a/data/curated/calibration/gbd-anchored/multi_crop_cost.csv +++ b/data/curated/calibration/gbd-anchored/multi_crop_cost.csv @@ -1,102 +1,102 @@ combination,country,correction_bnusd_per_mha -cotton_wheat,AFG,-1.6016085147857666 -cotton_wheat,AGO,-1.9636707305908203 +cotton_wheat,AFG,-1.5647928714752197 +cotton_wheat,AGO,-1.9165204763412476 cotton_wheat,ALB,7.5 cotton_wheat,ARG,7.5 cotton_wheat,ARM,7.5 -cotton_wheat,AUS,-6.347338676452637 +cotton_wheat,AUS,-5.982196092605591 cotton_wheat,AZE,7.5 -cotton_wheat,BDI,-1.0001932382583618 -cotton_wheat,BGD,-1.6351697444915771 +cotton_wheat,BDI,-0.9906471371650696 +cotton_wheat,BGD,-4.409899413585663 cotton_wheat,BGR,7.5 cotton_wheat,BOL,7.5 -cotton_wheat,BRA,-2.795017957687378 -cotton_wheat,BWA,-1.389197826385498 -cotton_wheat,CHN,-6.734447956085205 -cotton_wheat,COD,-1.943974256515503 +cotton_wheat,BRA,-2.630089044570923 +cotton_wheat,BWA,-1.3342844247817993 +cotton_wheat,CHN,-6.588923931121826 +cotton_wheat,COD,-1.926019310951233 cotton_wheat,DZA,7.5 -cotton_wheat,EGY,-3.046511173248291 -cotton_wheat,ESP,-1.8644442558288574 -cotton_wheat,ETH,-2.5176395177841187 +cotton_wheat,EGY,-2.5022599697113037 +cotton_wheat,ESP,-1.3596466183662415 +cotton_wheat,ETH,-2.494804859161377 cotton_wheat,GEO,7.5 -cotton_wheat,GRC,-2.5224956274032593 -cotton_wheat,HND,-2.0235610008239746 -cotton_wheat,IND,-1.663497805595398 -cotton_wheat,IRN,-2.413421869277954 -cotton_wheat,IRQ,-1.8790355920791626 -cotton_wheat,ISR,-0.5844569504261017 -cotton_wheat,JOR,-2.261714220046997 -cotton_wheat,KAZ,-2.105399250984192 -cotton_wheat,KEN,-0.7825355529785156 -cotton_wheat,KGZ,-1.4670000076293945 +cotton_wheat,GRC,-2.5894020199775696 +cotton_wheat,HND,-1.985054612159729 +cotton_wheat,IND,-1.6098428964614868 +cotton_wheat,IRN,-2.3573697805404663 +cotton_wheat,IRQ,-1.818800449371338 +cotton_wheat,ISR,-0.5333049893379211 +cotton_wheat,JOR,-1.5596385598182678 +cotton_wheat,KAZ,-1.0975533723831177 +cotton_wheat,KEN,-0.7460316717624664 +cotton_wheat,KGZ,-1.3961364030838013 cotton_wheat,KOR,7.5 cotton_wheat,LBN,7.5 -cotton_wheat,LSO,3.2752625644207 -cotton_wheat,MAR,-3.012405753135681 -cotton_wheat,MDG,-1.7472525835037231 -cotton_wheat,MEX,-3.5203187465667725 -cotton_wheat,MKD,-1.780644416809082 +cotton_wheat,LSO,3.298005998134613 +cotton_wheat,MAR,-1.4320390224456787 +cotton_wheat,MDG,-1.7152574062347412 +cotton_wheat,MEX,-3.411973476409912 +cotton_wheat,MKD,-1.251978874206543 cotton_wheat,MMR,7.5 -cotton_wheat,MOZ,-1.4387832283973694 -cotton_wheat,MWI,2.7791829705238342 -cotton_wheat,NAM,-2.076709508895874 -cotton_wheat,NER,-0.8645138144493103 -cotton_wheat,NGA,-1.9162575006484985 +cotton_wheat,MOZ,-1.4008106589317322 +cotton_wheat,MWI,2.7849952578544617 +cotton_wheat,NAM,-2.030031681060791 +cotton_wheat,NER,-0.815197765827179 +cotton_wheat,NGA,-1.8911105394363403 cotton_wheat,NPL,7.5 cotton_wheat,OMN,-0.0 -cotton_wheat,PAK,-1.3370583057403564 +cotton_wheat,PAK,-1.2887355089187622 cotton_wheat,PRT,7.5 -cotton_wheat,PRY,-0.814936101436615 -cotton_wheat,PSE,-1.8815619945526123 +cotton_wheat,PRY,-0.7549903690814972 +cotton_wheat,PSE,-1.817618489265442 cotton_wheat,ROU,-0.0 cotton_wheat,RUS,7.5 -cotton_wheat,RWA,-1.6137886047363281 -cotton_wheat,SDN,-1.7738453149795532 -cotton_wheat,SOM,2.7912174463272095 -cotton_wheat,SWZ,-1.798463225364685 -cotton_wheat,SYR,-1.6039836406707764 -cotton_wheat,TJK,-3.2277636528015137 -cotton_wheat,TKM,-1.6811714172363281 +cotton_wheat,RWA,-1.5886473655700684 +cotton_wheat,SDN,-1.7362133264541626 +cotton_wheat,SOM,2.7987120747566223 +cotton_wheat,SWZ,-1.7805107831954956 +cotton_wheat,SYR,-1.5369689464569092 +cotton_wheat,TJK,-3.2391955852508545 +cotton_wheat,TKM,-1.6322025060653687 cotton_wheat,TUN,7.5 -cotton_wheat,TUR,-1.2099840641021729 -cotton_wheat,TZA,-1.5156616568565369 -cotton_wheat,UGA,-1.794050395488739 +cotton_wheat,TUR,-1.08962082862854 +cotton_wheat,TZA,-1.4957929253578186 +cotton_wheat,UGA,-1.7642678022384644 cotton_wheat,URY,7.5 -cotton_wheat,USA,-1.561903715133667 -cotton_wheat,UZB,-1.8172263503074646 -cotton_wheat,YEM,-2.3821864128112793 -cotton_wheat,ZAF,-3.1744508743286133 +cotton_wheat,USA,-1.4530103206634521 +cotton_wheat,UZB,-1.7594308257102966 +cotton_wheat,YEM,-1.559592843055725 +cotton_wheat,ZAF,-2.487683057785034 cotton_wheat,ZMB,7.5 -cotton_wheat,ZWE,3.732309103012085 +cotton_wheat,ZWE,3.7777748107910156 double_rice,AFG,7.5 double_rice,AGO,7.5 double_rice,ALB,-0.0 double_rice,ARG,7.5 double_rice,AUS,7.5 double_rice,AZE,7.5 -double_rice,BDI,3.4612558484077454 -double_rice,BEN,-2.1600229740142822 -double_rice,BFA,-2.2092283964157104 -double_rice,BGD,-0.8059143722057343 -double_rice,BLZ,2.121865391731262 +double_rice,BDI,3.467129707336426 +double_rice,BEN,-2.8602006435394287 +double_rice,BFA,-2.1936237812042236 +double_rice,BGD,-2.3530810177326202 +double_rice,BLZ,2.130754828453064 double_rice,BOL,7.5 double_rice,BRA,7.5 -double_rice,BRN,-2.9540343284606934 +double_rice,BRN,-2.947162628173828 double_rice,BTN,7.5 double_rice,BWA,-0.0 double_rice,CAF,7.5 double_rice,CHL,7.5 -double_rice,CHN,-3.8152025938034058 +double_rice,CHN,-3.7860196828842163 double_rice,CIV,7.5 -double_rice,CMR,-2.14290189743042 +double_rice,CMR,-2.12709903717041 double_rice,COD,7.5 double_rice,COG,7.5 -double_rice,COL,-2.7943174839019775 -double_rice,CRI,-1.9651430249214172 +double_rice,COL,-2.777014136314392 +double_rice,CRI,-1.9361964464187622 double_rice,CUB,7.5 -double_rice,DOM,2.0134849548339844 +double_rice,DOM,1.9726519584655762 double_rice,DZA,7.5 -double_rice,ECU,-1.429942786693573 +double_rice,ECU,-1.4168822765350342 double_rice,EGY,7.5 double_rice,ERI,7.5 double_rice,ESP,7.5 @@ -105,390 +105,390 @@ double_rice,FJI,7.5 double_rice,FRA,7.5 double_rice,GAB,7.5 double_rice,GEO,7.5 -double_rice,GHA,-0.8484333753585815 -double_rice,GIN,-1.1149972677230835 -double_rice,GMB,-0.03029663860797882 -double_rice,GNB,-2.5158348083496094 +double_rice,GHA,-0.829657793045044 +double_rice,GIN,-7.5 +double_rice,GMB,-0.017006026580929756 +double_rice,GNB,-2.504655122756958 double_rice,GNQ,7.5 double_rice,GRC,7.5 -double_rice,GTM,2.2835614681243896 +double_rice,GTM,2.2880258560180664 double_rice,GUF,7.5 double_rice,GUY,7.5 double_rice,HND,7.5 -double_rice,HTI,2.4255069494247437 -double_rice,IDN,-2.813283920288086 -double_rice,IND,-1.7255062460899353 +double_rice,HTI,2.432379364967346 +double_rice,IDN,-2.8577184677124023 +double_rice,IND,-1.7067153453826904 double_rice,IRN,7.5 double_rice,IRQ,7.5 double_rice,ITA,7.5 -double_rice,JPN,-7.4767537117004395 +double_rice,JPN,-7.419216156005859 double_rice,KEN,7.5 -double_rice,KHM,-2.6360760927200317 +double_rice,KHM,-2.616986870765686 double_rice,KOR,0.0 -double_rice,LAO,-2.5891036987304688 +double_rice,LAO,-2.568940043449402 double_rice,LBR,7.5 -double_rice,LKA,3.291220009326935 +double_rice,LKA,3.299778699874878 double_rice,LSO,-0.0 double_rice,MAR,7.5 -double_rice,MDG,3.556140199303627 +double_rice,MDG,3.563939854502678 double_rice,MEX,7.5 double_rice,MKD,7.5 -double_rice,MLI,-0.19956199079751968 -double_rice,MMR,-2.759416341781616 +double_rice,MLI,-0.17661113291978836 +double_rice,MMR,-2.7423404455184937 double_rice,MOZ,7.5 -double_rice,MRT,-1.7148674726486206 +double_rice,MRT,-1.6928284168243408 double_rice,MUS,7.5 -double_rice,MWI,-1.9178258180618286 -double_rice,MYS,-0.9908324480056763 +double_rice,MWI,-1.9064983129501343 +double_rice,MYS,-0.9760869145393372 double_rice,NAM,7.5 double_rice,NER,7.5 -double_rice,NGA,-0.4380754977464676 -double_rice,NIC,-0.3926215171813965 -double_rice,NPL,3.6133005023002625 +double_rice,NGA,-0.4188295602798462 +double_rice,NIC,2.059400498867035 +double_rice,NPL,3.624129503965378 double_rice,PAK,7.5 -double_rice,PAN,-2.2472994327545166 +double_rice,PAN,-2.227658271789551 double_rice,PER,7.5 -double_rice,PHL,-1.7187378406524658 +double_rice,PHL,-1.702743947505951 double_rice,PNG,7.5 double_rice,PRI,7.5 double_rice,PRT,7.5 double_rice,PRY,7.5 -double_rice,RWA,-3.4841092824935913 +double_rice,RWA,-3.457836627960205 double_rice,SAU,7.5 double_rice,SDN,7.5 -double_rice,SEN,0.23442745953798294 +double_rice,SEN,0.2560163214802742 double_rice,SLB,7.5 -double_rice,SLE,3.599737837910652 +double_rice,SLE,3.6082591861486435 double_rice,SLV,7.5 double_rice,SOM,7.5 double_rice,SSD,7.5 double_rice,SUR,7.5 double_rice,SWZ,7.5 double_rice,SYR,7.5 -double_rice,TCD,-1.106724500656128 -double_rice,TGO,-0.24520094692707062 -double_rice,THA,-0.89333575963974 +double_rice,TCD,-1.0949281454086304 +double_rice,TGO,-0.2319807931780815 +double_rice,THA,-0.8785071969032288 double_rice,TJK,7.5 double_rice,TKM,7.5 double_rice,TLS,7.5 double_rice,TTO,7.5 double_rice,TUN,-0.0 double_rice,TUR,7.5 -double_rice,TWN,-1.9572516083717346 +double_rice,TWN,-1.926475167274475 double_rice,TZA,7.5 double_rice,UGA,7.5 double_rice,URY,7.5 double_rice,USA,7.5 double_rice,UZB,7.5 double_rice,VEN,7.5 -double_rice,VNM,-2.058097720146179 +double_rice,VNM,-2.036122441291809 double_rice,YEM,-0.0 double_rice,ZAF,7.5 double_rice,ZMB,7.5 double_rice,ZWE,7.5 -maize_soybean,AFG,-0.022748412564396858 -maize_soybean,AGO,-1.453369140625 +maize_soybean,AFG,-0.024129193276166916 +maize_soybean,AGO,-1.4524705410003662 maize_soybean,ALB,7.5 -maize_soybean,ARG,0.13026892486959696 +maize_soybean,ARG,0.14385014958679676 maize_soybean,ARM,7.5 -maize_soybean,AUS,1.7598615884780884 -maize_soybean,AUT,4.7173022627830505 -maize_soybean,AZE,0.5006841421127319 -maize_soybean,BDI,-1.0802985429763794 +maize_soybean,AUS,1.781106948852539 +maize_soybean,AUT,4.7361374497413635 +maize_soybean,AZE,1.3419886827468872 +maize_soybean,BDI,-1.078061044216156 maize_soybean,BEL,7.5 -maize_soybean,BEN,-0.7781942188739777 -maize_soybean,BFA,-0.5925497114658356 +maize_soybean,BEN,-0.7773599922657013 +maize_soybean,BFA,-0.5899175703525543 maize_soybean,BGD,7.5 maize_soybean,BGR,7.5 maize_soybean,BIH,7.5 -maize_soybean,BLZ,-1.4516592025756836 -maize_soybean,BOL,3.6931674145162106 -maize_soybean,BRA,-0.05819132551550865 -maize_soybean,BTN,4.0696980357170105 -maize_soybean,BWA,0.20446154475212097 +maize_soybean,BLZ,-1.444799780845642 +maize_soybean,BOL,3.6947455555200577 +maize_soybean,BRA,-0.08396416902542114 +maize_soybean,BTN,4.167992889881134 +maize_soybean,BWA,0.2133101373910904 maize_soybean,CAF,-7.5 -maize_soybean,CAN,-0.4329428970813751 +maize_soybean,CAN,-0.3356585204601288 maize_soybean,CHE,7.5 -maize_soybean,CHN,-1.761126160621643 -maize_soybean,CIV,-0.941570907831192 -maize_soybean,CMR,-1.040739357471466 -maize_soybean,COD,-1.622969388961792 -maize_soybean,COG,-1.5172104239463806 -maize_soybean,COL,-0.3639393150806427 -maize_soybean,CRI,-0.8879108428955078 +maize_soybean,CHN,-1.5403509140014648 +maize_soybean,CIV,-0.9388851523399353 +maize_soybean,CMR,-1.0390645861625671 +maize_soybean,COD,-1.6217492818832397 +maize_soybean,COG,-1.5153383016586304 +maize_soybean,COL,-0.3526015728712082 +maize_soybean,CRI,-0.8843345046043396 maize_soybean,CUB,7.5 maize_soybean,CZE,7.5 maize_soybean,DEU,7.5 maize_soybean,DZA,-0.0 -maize_soybean,ECU,-0.8186103105545044 -maize_soybean,EGY,-2.090902805328369 -maize_soybean,ESP,-0.9297317862510681 -maize_soybean,ETH,-0.03956172242760658 +maize_soybean,ECU,-0.8080757260322571 +maize_soybean,EGY,-1.5441534519195557 +maize_soybean,ESP,-0.4190533757209778 +maize_soybean,ETH,-0.034022461622953415 maize_soybean,FRA,7.5 -maize_soybean,GAB,-1.2612962126731873 -maize_soybean,GEO,0.280986487865448 -maize_soybean,GHA,-0.7917667627334595 +maize_soybean,GAB,-1.2567499876022339 +maize_soybean,GEO,0.28025704622268677 +maize_soybean,GHA,-0.7886674702167511 maize_soybean,GIN,7.5 -maize_soybean,GRC,2.78367817401886 -maize_soybean,GTM,-0.45622143149375916 -maize_soybean,HND,-0.27370449900627136 +maize_soybean,GRC,3.140765070915222 +maize_soybean,GTM,-0.4559568464756012 +maize_soybean,HND,-0.27266019582748413 maize_soybean,HRV,7.5 -maize_soybean,HUN,0.5368187390267849 -maize_soybean,IDN,0.14225086569786072 -maize_soybean,IND,-0.6644619703292847 -maize_soybean,IRN,-0.15079118311405182 -maize_soybean,IRQ,-0.6124063618481159 +maize_soybean,HUN,-0.11800742521882057 +maize_soybean,IDN,0.172398179769516 +maize_soybean,IND,-0.6594437658786774 +maize_soybean,IRN,-0.1419895589351654 +maize_soybean,IRQ,-0.6025996413081884 maize_soybean,ISR,7.5 maize_soybean,ITA,7.5 maize_soybean,JOR,-0.0 maize_soybean,KAZ,7.5 -maize_soybean,KEN,-0.438772976398468 -maize_soybean,KGZ,-0.3382623493671417 -maize_soybean,KHM,-0.6917684078216553 -maize_soybean,KOR,-0.4339219331741333 -maize_soybean,LAO,-0.08379296539351344 +maize_soybean,KEN,-0.4362334907054901 +maize_soybean,KGZ,-0.3236069679260254 +maize_soybean,KHM,-0.6810276210308075 +maize_soybean,KOR,-0.42408648133277893 +maize_soybean,LAO,-0.07201255613472313 maize_soybean,LBN,7.5 -maize_soybean,LKA,-0.8904912769794464 +maize_soybean,LKA,-0.8855485618114471 maize_soybean,LSO,7.5 maize_soybean,LUX,7.5 maize_soybean,MAR,7.5 maize_soybean,MDA,7.5 -maize_soybean,MEX,0.1641877219080925 +maize_soybean,MEX,0.15553955733776093 maize_soybean,MKD,7.5 -maize_soybean,MLI,-0.8014289140701294 -maize_soybean,MMR,-1.0643283128738403 +maize_soybean,MLI,-0.7947446703910828 +maize_soybean,MMR,-1.0605445504188538 maize_soybean,MNE,7.5 -maize_soybean,MOZ,3.9535765796899796 -maize_soybean,MWI,3.600542798638344 -maize_soybean,NGA,-0.7509967088699341 -maize_soybean,NIC,-0.4151580035686493 +maize_soybean,MOZ,3.9519665092229843 +maize_soybean,MWI,3.60207137465477 +maize_soybean,NGA,-0.7484491467475891 +maize_soybean,NIC,0.3136984258890152 maize_soybean,NLD,7.5 -maize_soybean,NPL,3.7117144651710987 -maize_soybean,PAK,-0.7835625112056732 +maize_soybean,NPL,3.7145150117576122 +maize_soybean,PAK,-0.7764266431331635 maize_soybean,PER,7.5 maize_soybean,POL,7.5 maize_soybean,PRT,7.5 -maize_soybean,PRY,-0.41487105190753937 -maize_soybean,ROU,4.206701397895813 +maize_soybean,PRY,-0.4078255891799927 +maize_soybean,ROU,4.1359822154045105 maize_soybean,RUS,7.5 -maize_soybean,RWA,-0.35534021258354187 -maize_soybean,SDN,-0.33095285296440125 -maize_soybean,SLV,-0.8271979987621307 +maize_soybean,RWA,-0.3528798222541809 +maize_soybean,SDN,-0.32907384634017944 +maize_soybean,SLV,-0.8237781226634979 maize_soybean,SRB,7.5 maize_soybean,SSD,7.5 maize_soybean,SVK,7.5 maize_soybean,SVN,7.5 -maize_soybean,SWZ,-0.7634835839271545 +maize_soybean,SWZ,-0.7595874071121216 maize_soybean,SYR,7.5 -maize_soybean,TCD,3.591791406273842 -maize_soybean,TGO,-0.3725236654281616 -maize_soybean,THA,-0.4974634647369385 +maize_soybean,TCD,3.5924229472875595 +maize_soybean,TGO,-0.3709297478199005 +maize_soybean,THA,-0.4897614121437073 maize_soybean,TJK,7.5 maize_soybean,TKM,7.5 -maize_soybean,TLS,4.1178505420684814 +maize_soybean,TLS,4.124555975198746 maize_soybean,TUR,7.5 maize_soybean,TWN,-0.0 -maize_soybean,TZA,-0.34078487753868103 -maize_soybean,UGA,-1.4062016010284424 +maize_soybean,TZA,-0.33904388546943665 +maize_soybean,UGA,-1.4028520584106445 maize_soybean,UKR,7.5 -maize_soybean,URY,0.045277222990989685 -maize_soybean,USA,-0.6260630488395691 +maize_soybean,URY,0.05690854787826538 +maize_soybean,USA,-0.5385132431983948 maize_soybean,UZB,7.5 -maize_soybean,VEN,-0.8716048002243042 -maize_soybean,VNM,-0.35146464407444 -maize_soybean,ZAF,-0.2168479636311531 -maize_soybean,ZMB,-0.5572142601013184 -maize_soybean,ZWE,1.0526694059371948 -rice_maize,AFG,-1.7495805025100708 -rice_maize,AGO,-2.388108730316162 +maize_soybean,VEN,-0.8645250499248505 +maize_soybean,VNM,-0.3363178223371506 +maize_soybean,ZAF,-0.04609121195971966 +maize_soybean,ZMB,-0.554208517074585 +maize_soybean,ZWE,1.0462255477905273 +rice_maize,AFG,-1.7349820137023926 +rice_maize,AGO,-2.3834211826324463 rice_maize,ALB,-0.0 rice_maize,ARG,7.5 rice_maize,ARM,-0.0 -rice_maize,AUS,-2.3311867713928223 -rice_maize,AZE,-3.8599894046783447 -rice_maize,BDI,-0.733100175857544 -rice_maize,BEN,-1.646537959575653 -rice_maize,BGD,0.32678547501564026 +rice_maize,AUS,-1.8098127841949463 +rice_maize,AZE,-2.862872838973999 +rice_maize,BDI,-0.7241317629814148 +rice_maize,BEN,-1.6333386898040771 +rice_maize,BGD,-7.203069686889648 rice_maize,BGR,7.5 -rice_maize,BLZ,-2.670062303543091 +rice_maize,BLZ,-2.652860403060913 rice_maize,BOL,7.5 -rice_maize,BRA,-0.29058557748794556 -rice_maize,BTN,-0.6720845103263855 +rice_maize,BRA,-0.2512147128582001 +rice_maize,BTN,-0.3047730103135109 rice_maize,BWA,-0.0 -rice_maize,CAF,-2.3995039463043213 +rice_maize,CAF,-2.3949413299560547 rice_maize,CAN,-0.0 -rice_maize,CHL,-1.947529673576355 -rice_maize,CHN,-3.307382345199585 -rice_maize,CIV,-0.9656861424446106 +rice_maize,CHL,-1.90910005569458 +rice_maize,CHN,-3.225025177001953 +rice_maize,CIV,-0.9540694952011108 rice_maize,CMR,7.5 -rice_maize,COD,-2.518190383911133 -rice_maize,COL,-2.0998793840408325 -rice_maize,COM,-2.4091944694519043 +rice_maize,COD,-2.5134639739990234 +rice_maize,COL,-2.0789473056793213 +rice_maize,COM,-2.404522657394409 rice_maize,CUB,7.5 -rice_maize,DOM,2.308054804801941 +rice_maize,DOM,2.2634270191192627 rice_maize,DZA,7.5 -rice_maize,ECU,-0.8900418877601624 -rice_maize,EGY,-3.1733345985412598 +rice_maize,ECU,-0.8620496988296509 +rice_maize,EGY,-2.6688413619995117 rice_maize,ERI,7.5 -rice_maize,ESP,-3.5793111324310303 +rice_maize,ESP,-2.0917372703552246 rice_maize,ETH,7.5 rice_maize,FRA,7.5 rice_maize,GEO,7.5 -rice_maize,GHA,-1.031973958015442 -rice_maize,GIN,-0.7264726459980011 -rice_maize,GNB,-1.6693933010101318 -rice_maize,GRC,-3.095834970474243 -rice_maize,GTM,-2.2746835947036743 -rice_maize,HND,-1.8321233987808228 -rice_maize,HTI,-2.2494877576828003 +rice_maize,GHA,-1.0192705392837524 +rice_maize,GIN,-0.9213454723358154 +rice_maize,GNB,-1.6536740064620972 +rice_maize,GRC,-2.2891706228256226 +rice_maize,GTM,-2.2656397819519043 +rice_maize,HND,-1.8203611373901367 +rice_maize,HTI,-2.240273118019104 rice_maize,HUN,7.5 rice_maize,IDN,-0.0 -rice_maize,IND,-1.4378907680511475 -rice_maize,IRN,-4.248165130615234 -rice_maize,IRQ,-2.43397319316864 -rice_maize,ITA,-3.4090006351470947 -rice_maize,KAZ,-1.0813347697257996 -rice_maize,KEN,-1.3619197010993958 -rice_maize,KGZ,-3.5008654594421387 -rice_maize,KOR,-6.912171363830566 -rice_maize,LAO,-1.7018980979919434 -rice_maize,LKA,-1.307216763496399 +rice_maize,IND,-1.4158118963241577 +rice_maize,IRN,-4.222367525100708 +rice_maize,IRQ,-2.4123810529708862 +rice_maize,ITA,-7.196671962738037 +rice_maize,KAZ,0.01209117635153234 +rice_maize,KEN,-1.9142131209373474 +rice_maize,KGZ,-3.475287437438965 +rice_maize,KOR,-6.895806789398193 +rice_maize,LAO,-1.6798478364944458 +rice_maize,LKA,-1.2938867211341858 rice_maize,LSO,-0.0 -rice_maize,MAR,2.597246766090393 +rice_maize,MAR,0.39889025688171387 rice_maize,MDA,7.5 -rice_maize,MDG,-1.279721975326538 -rice_maize,MEX,-0.6952770352363586 +rice_maize,MDG,-1.2718197107315063 +rice_maize,MEX,-0.6624983847141266 rice_maize,MKD,7.5 -rice_maize,MLI,-0.6271843314170837 -rice_maize,MMR,-1.670688509941101 -rice_maize,MOZ,-0.5146883130073547 +rice_maize,MLI,-0.6122713088989258 +rice_maize,MMR,-1.652342438697815 +rice_maize,MOZ,-0.5099467635154724 rice_maize,MUS,7.5 -rice_maize,MWI,-1.3178186416625977 +rice_maize,MWI,-1.3106465339660645 rice_maize,NAM,7.5 -rice_maize,NGA,-0.7409452497959137 +rice_maize,NGA,-0.7293228507041931 rice_maize,NIC,-0.0 -rice_maize,NPL,-0.5504612028598785 -rice_maize,PAK,-1.49882173538208 -rice_maize,PER,-3.9759881496429443 -rice_maize,PHL,-1.1747495532035828 +rice_maize,NPL,-0.5354790985584259 +rice_maize,PAK,-1.4768998622894287 +rice_maize,PER,-3.395177125930786 +rice_maize,PHL,-1.1566152572631836 rice_maize,PRI,7.5 -rice_maize,PRT,2.9210872650146484 -rice_maize,PRY,-1.1517144441604614 -rice_maize,ROU,-1.4203614592552185 +rice_maize,PRT,2.940045654773712 +rice_maize,PRY,-1.1339808702468872 +rice_maize,ROU,-1.617322862148285 rice_maize,RUS,7.5 -rice_maize,RWA,-2.0939033031463623 +rice_maize,RWA,-2.076981544494629 rice_maize,SAU,7.5 -rice_maize,SDN,-2.53087055683136 +rice_maize,SDN,-2.5269668102264404 rice_maize,SEN,7.5 rice_maize,SLE,7.5 -rice_maize,SLV,-3.1758580207824707 -rice_maize,SOM,-0.5762790441513062 -rice_maize,SSD,-2.371057868003845 +rice_maize,SLV,-3.188874125480652 +rice_maize,SOM,-0.5467300415039062 +rice_maize,SSD,-2.3679414987564087 rice_maize,SVK,-0.0 rice_maize,SVN,-0.0 rice_maize,SWZ,7.5 -rice_maize,SYR,-0.661085844039917 -rice_maize,TCD,-0.541948676109314 -rice_maize,TGO,-0.4622090458869934 -rice_maize,THA,-0.989659309387207 -rice_maize,TJK,-3.904982805252075 -rice_maize,TKM,-1.3028086423873901 -rice_maize,TLS,-1.3998390436172485 +rice_maize,SYR,-0.6277920603752136 +rice_maize,TCD,-0.5307839512825012 +rice_maize,TGO,-0.45374345779418945 +rice_maize,THA,-0.9743051826953888 +rice_maize,TJK,-3.919065237045288 +rice_maize,TKM,-1.2833765149116516 +rice_maize,TLS,-1.3792622089385986 rice_maize,TUN,-0.0 -rice_maize,TUR,-3.013861298561096 -rice_maize,TZA,-0.08381067216396332 -rice_maize,UGA,-2.3060479164123535 +rice_maize,TUR,-2.8991039991378784 +rice_maize,TZA,-0.14717228710651398 +rice_maize,UGA,-2.2997924089431763 rice_maize,UKR,7.5 -rice_maize,URY,-0.8209086060523987 -rice_maize,USA,-1.962952733039856 -rice_maize,UZB,-6.036947727203369 -rice_maize,VEN,-1.313603401184082 -rice_maize,VNM,-1.380617380142212 +rice_maize,URY,-0.783143937587738 +rice_maize,USA,-1.890041470527649 +rice_maize,UZB,-6.01021671295166 +rice_maize,VEN,-1.2891035676002502 +rice_maize,VNM,-1.3532211780548096 rice_maize,YEM,-0.0 -rice_maize,ZAF,-1.4254977107048035 -rice_maize,ZMB,-1.1254773139953613 +rice_maize,ZAF,-0.9386002123355865 +rice_maize,ZMB,-1.1152955293655396 rice_maize,ZWE,7.5 -rice_wheat,AFG,-1.3534507155418396 +rice_wheat,AFG,-1.337408721446991 rice_wheat,AGO,7.5 rice_wheat,ALB,-0.0 rice_wheat,ARG,7.5 rice_wheat,ARM,-0.0 -rice_wheat,AUS,-2.1173155307769775 -rice_wheat,AZE,-3.3311681747436523 -rice_wheat,BGD,-0.760441780090332 -rice_wheat,BGR,2.800064980983734 +rice_wheat,AUS,-1.6618328094482422 +rice_wheat,AZE,-2.7145884037017822 +rice_wheat,BGD,-4.662951946258545 +rice_wheat,BGR,2.8267303705215454 rice_wheat,BOL,7.5 rice_wheat,BRA,7.5 -rice_wheat,BTN,-0.25281507743056864 +rice_wheat,BTN,0.10427264124155045 rice_wheat,BWA,-0.0 rice_wheat,CAN,-0.0 -rice_wheat,CHL,-1.4021535515785217 -rice_wheat,CHN,-3.20658016204834 -rice_wheat,CMR,-1.7350846529006958 -rice_wheat,COD,-2.2580342292785645 +rice_wheat,CHL,-1.3720502257347107 +rice_wheat,CHN,-3.14938747882843 +rice_wheat,CMR,-1.724717140197754 +rice_wheat,COD,-2.25276780128479 rice_wheat,DZA,7.5 -rice_wheat,ECU,-1.4204405546188354 -rice_wheat,EGY,-2.800849676132202 -rice_wheat,ESP,-2.255333185195923 +rice_wheat,ECU,-1.4145389795303345 +rice_wheat,EGY,-2.37221360206604 +rice_wheat,ESP,-1.2849757671356201 rice_wheat,ETH,7.5 rice_wheat,FRA,7.5 rice_wheat,GEO,7.5 -rice_wheat,GRC,-1.6955810189247131 +rice_wheat,GRC,-1.1603538691997528 rice_wheat,GTM,7.5 -rice_wheat,HND,-2.086721658706665 +rice_wheat,HND,-2.0786519050598145 rice_wheat,HUN,7.5 -rice_wheat,IND,-1.2840489149093628 -rice_wheat,IRN,-3.555416226387024 -rice_wheat,IRQ,-2.129554271697998 -rice_wheat,ITA,-2.6914992332458496 -rice_wheat,JPN,-0.9766173958778381 -rice_wheat,KAZ,-1.0575806498527527 -rice_wheat,KGZ,-3.2925446033477783 -rice_wheat,KOR,-6.724577903747559 +rice_wheat,IND,-1.262333333492279 +rice_wheat,IRN,-3.537307024002075 +rice_wheat,IRQ,-2.113009214401245 +rice_wheat,ITA,-4.885395765304565 +rice_wheat,JPN,-0.9923773407936096 +rice_wheat,KAZ,-0.36993010342121124 +rice_wheat,KGZ,-3.2788922786712646 +rice_wheat,KOR,-6.709107875823975 rice_wheat,LAO,-0.0 rice_wheat,LSO,-0.0 -rice_wheat,MAR,-2.4409526586532593 +rice_wheat,MAR,-3.6098453998565674 rice_wheat,MDA,7.5 -rice_wheat,MDG,-0.9923009872436523 +rice_wheat,MDG,-0.9835709929466248 rice_wheat,MEX,7.5 rice_wheat,MKD,7.5 -rice_wheat,MLI,-0.5925939083099365 -rice_wheat,MMR,-1.849918246269226 +rice_wheat,MLI,-0.5748081207275391 +rice_wheat,MMR,-1.8346061706542969 rice_wheat,MOZ,7.5 rice_wheat,MRT,7.5 -rice_wheat,MWI,2.8602685928344727 -rice_wheat,NAM,2.656647562980652 -rice_wheat,NER,-1.4336342811584473 -rice_wheat,NGA,-0.8298945426940918 -rice_wheat,NPL,-0.7108609080314636 -rice_wheat,PAK,-1.4355943202972412 -rice_wheat,PRT,-1.3605966567993164 -rice_wheat,PRY,-0.9652633965015411 +rice_wheat,MWI,2.8630940318107605 +rice_wheat,NAM,2.6641334295272827 +rice_wheat,NER,-1.4156060218811035 +rice_wheat,NGA,-0.8165081739425659 +rice_wheat,NPL,-0.6977772414684296 +rice_wheat,PAK,-1.420112133026123 +rice_wheat,PRT,-1.3402419090270996 +rice_wheat,PRY,-0.9523392915725708 rice_wheat,ROU,7.5 rice_wheat,RUS,7.5 -rice_wheat,RWA,-2.2284631729125977 -rice_wheat,SDN,-1.6409181356430054 +rice_wheat,RWA,-2.213728666305542 +rice_wheat,SDN,-1.6270463466644287 rice_wheat,SOM,7.5 rice_wheat,SVK,-0.0 rice_wheat,SVN,-0.0 rice_wheat,SWZ,7.5 -rice_wheat,SYR,-0.8439756631851196 -rice_wheat,TCD,-0.9173677265644073 +rice_wheat,SYR,-0.8183375000953674 +rice_wheat,TCD,-0.9052500128746033 rice_wheat,THA,7.5 -rice_wheat,TJK,-3.6388649940490723 -rice_wheat,TKM,-1.3595415353775024 +rice_wheat,TJK,-3.6782443523406982 +rice_wheat,TKM,-1.344571828842163 rice_wheat,TUN,-0.0 -rice_wheat,TUR,-2.6333714723587036 -rice_wheat,TZA,-0.5026793479919434 -rice_wheat,UGA,-2.0544402599334717 +rice_wheat,TUR,-2.5926767587661743 +rice_wheat,TZA,-0.49236419796943665 +rice_wheat,UGA,-2.048445463180542 rice_wheat,UKR,7.5 -rice_wheat,URY,-0.6990396976470947 -rice_wheat,USA,-1.5776442289352417 -rice_wheat,UZB,-4.868905544281006 +rice_wheat,URY,-0.669433206319809 +rice_wheat,USA,-1.5447631478309631 +rice_wheat,UZB,-4.849060535430908 rice_wheat,VEN,7.5 -rice_wheat,VNM,-1.5102684497833252 -rice_wheat,ZAF,-1.5638744831085205 -rice_wheat,ZMB,-0.8846746683120728 +rice_wheat,VNM,-1.4935694336891174 +rice_wheat,ZAF,-1.1985734701156616 +rice_wheat,ZMB,-0.8680102825164795 rice_wheat,ZWE,7.5 triple_rice,AFG,7.5 triple_rice,AGO,7.5 @@ -497,12 +497,12 @@ triple_rice,AUS,7.5 triple_rice,BDI,7.5 triple_rice,BEN,7.5 triple_rice,BFA,7.5 -triple_rice,BGD,-0.6115118265151978 +triple_rice,BGD,-7.5 triple_rice,BLZ,7.5 triple_rice,BOL,7.5 triple_rice,BRA,7.5 triple_rice,BRN,7.5 -triple_rice,BTN,-2.8461802005767822 +triple_rice,BTN,-2.098270297050476 triple_rice,BWA,-0.0 triple_rice,CHN,7.5 triple_rice,CIV,7.5 @@ -527,7 +527,7 @@ triple_rice,GMB,7.5 triple_rice,GNB,7.5 triple_rice,GNQ,7.5 triple_rice,GRC,7.5 -triple_rice,GTM,1.5453910827636719 +triple_rice,GTM,1.5519988536834717 triple_rice,GUF,7.5 triple_rice,GUY,7.5 triple_rice,HND,7.5 @@ -538,7 +538,7 @@ triple_rice,IRN,7.5 triple_rice,IRQ,7.5 triple_rice,ITA,7.5 triple_rice,KEN,7.5 -triple_rice,KHM,-3.954114317893982 +triple_rice,KHM,-3.9254804849624634 triple_rice,LAO,7.5 triple_rice,LBR,7.5 triple_rice,LKA,7.5 @@ -568,7 +568,7 @@ triple_rice,RWA,7.5 triple_rice,SAU,7.5 triple_rice,SDN,7.5 triple_rice,SEN,7.5 -triple_rice,SLB,-4.486171245574951 +triple_rice,SLB,-4.474208831787109 triple_rice,SLE,7.5 triple_rice,SLV,7.5 triple_rice,SOM,7.5 @@ -578,7 +578,7 @@ triple_rice,SWZ,7.5 triple_rice,TCD,7.5 triple_rice,TGO,7.5 triple_rice,THA,7.5 -triple_rice,TLS,1.243781328201294 +triple_rice,TLS,1.2554755210876465 triple_rice,TTO,7.5 triple_rice,TUR,7.5 triple_rice,TWN,7.5 @@ -587,204 +587,204 @@ triple_rice,UGA,7.5 triple_rice,URY,7.5 triple_rice,USA,7.5 triple_rice,VEN,7.5 -triple_rice,VNM,2.532027840614319 +triple_rice,VNM,2.5555001497268677 triple_rice,YEM,-0.0 triple_rice,ZAF,7.5 triple_rice,ZMB,7.5 triple_rice,ZWE,7.5 -wheat_maize,AFG,-1.0099253356456757 -wheat_maize,AGO,-1.581254243850708 +wheat_maize,AFG,-0.9982629716396332 +wheat_maize,AGO,-1.5774325132369995 wheat_maize,ALB,7.5 wheat_maize,ARE,7.5 wheat_maize,ARG,7.5 -wheat_maize,ARM,-0.29412639141082764 -wheat_maize,AUS,0.04519475996494293 +wheat_maize,ARM,-0.2738838493824005 +wheat_maize,AUS,0.08059412986040115 wheat_maize,AUT,7.5 -wheat_maize,AZE,-0.8957000374794006 -wheat_maize,BDI,-1.1493258476257324 +wheat_maize,AZE,-0.278755247592926 +wheat_maize,BDI,-1.1442186832427979 wheat_maize,BEL,7.5 -wheat_maize,BGD,-0.15233726799488068 +wheat_maize,BGD,-3.114882245659828 wheat_maize,BGR,7.5 wheat_maize,BIH,7.5 -wheat_maize,BOL,-0.5905634164810181 -wheat_maize,BRA,-0.34408819675445557 -wheat_maize,BTN,1.08690744638443 -wheat_maize,BWA,-0.2176012247800827 +wheat_maize,BOL,-0.5814153552055359 +wheat_maize,BRA,-0.3353937566280365 +wheat_maize,BTN,1.3449606895446777 +wheat_maize,BWA,-0.19745498895645142 wheat_maize,CAN,7.5 wheat_maize,CHE,7.5 -wheat_maize,CHL,-1.643505871295929 -wheat_maize,CHN,-2.670019745826721 -wheat_maize,CMR,-0.944013923406601 -wheat_maize,COD,-1.5697588920593262 -wheat_maize,COL,-1.1596239805221558 +wheat_maize,CHL,-1.6047284603118896 +wheat_maize,CHN,-2.60494863986969 +wheat_maize,CMR,-0.9307748675346375 +wheat_maize,COD,-1.564340591430664 +wheat_maize,COL,-1.135996699333191 wheat_maize,CZE,7.5 wheat_maize,DEU,7.5 wheat_maize,DZA,7.5 -wheat_maize,ECU,-1.3109267950057983 -wheat_maize,EGY,-2.7034671306610107 -wheat_maize,ERI,-1.5133994221687317 -wheat_maize,ESP,-0.6335636377334595 -wheat_maize,ETH,-0.5352818369865417 +wheat_maize,ECU,-1.2982423305511475 +wheat_maize,EGY,-2.230076789855957 +wheat_maize,ERI,-1.5081796050071716 +wheat_maize,ESP,-0.5177604854106903 +wheat_maize,ETH,-0.5211540460586548 wheat_maize,FRA,7.5 -wheat_maize,GEO,-0.05703403800725937 -wheat_maize,GRC,2.745781183242798 -wheat_maize,GTM,3.237878978252411 -wheat_maize,HND,-1.2028067708015442 +wheat_maize,GEO,-0.03935140371322632 +wheat_maize,GRC,3.0105645060539246 +wheat_maize,GTM,3.2458449602127075 +wheat_maize,HND,-1.1957104206085205 wheat_maize,HRV,7.5 -wheat_maize,HUN,-0.4037407413125038 -wheat_maize,IND,-0.8158817291259766 -wheat_maize,IRN,-1.3398287892341614 -wheat_maize,IRQ,-1.4171549081802368 -wheat_maize,ISR,-6.781064987182617 -wheat_maize,ITA,3.0711528062820435 -wheat_maize,JOR,-0.8906932771205902 -wheat_maize,KAZ,-0.7657935619354248 -wheat_maize,KEN,-0.8532283902168274 -wheat_maize,KGZ,-0.5902541279792786 -wheat_maize,KOR,-0.40849533677101135 +wheat_maize,HUN,-0.7320578992366791 +wheat_maize,IND,-0.7993054986000061 +wheat_maize,IRN,-1.318627119064331 +wheat_maize,IRQ,-1.398362636566162 +wheat_maize,ISR,-6.784129858016968 +wheat_maize,ITA,2.002211570739746 +wheat_maize,JOR,-0.25102006644010544 +wheat_maize,KAZ,-0.013067563995718956 +wheat_maize,KEN,-0.8453209400177002 +wheat_maize,KGZ,-0.5661491751670837 +wheat_maize,KOR,-0.3896560072898865 wheat_maize,LAO,-0.0 -wheat_maize,LBN,-0.7102650701999664 +wheat_maize,LBN,-3.9107842445373535 wheat_maize,LBY,7.5 -wheat_maize,LSO,-0.0717716682702303 +wheat_maize,LSO,-0.06203848868608475 wheat_maize,LUX,7.5 -wheat_maize,MAR,-0.6269045472145081 +wheat_maize,MAR,-3.2030954360961914 wheat_maize,MDA,7.5 -wheat_maize,MDG,-1.2473762035369873 -wheat_maize,MEX,-1.2082997560501099 +wheat_maize,MDG,-1.2373456954956055 +wheat_maize,MEX,-1.1959062814712524 wheat_maize,MKD,7.5 -wheat_maize,MMR,-0.6819483935832977 +wheat_maize,MMR,-0.6656034886837006 wheat_maize,MNE,7.5 -wheat_maize,MOZ,-0.32281121611595154 -wheat_maize,MWI,3.352478265762329 -wheat_maize,NAM,-1.128983199596405 +wheat_maize,MOZ,-0.31333038210868835 +wheat_maize,MWI,3.3555931746959686 +wheat_maize,NAM,-1.1129449605941772 wheat_maize,NIC,7.5 wheat_maize,NLD,7.5 -wheat_maize,NPL,-0.6020831167697906 -wheat_maize,NZL,-1.8989965915679932 -wheat_maize,OMN,-4.073334693908691 -wheat_maize,PAK,-0.6937783062458038 -wheat_maize,PER,-1.516123354434967 +wheat_maize,NPL,-0.5895421802997589 +wheat_maize,NZL,-1.9291799068450928 +wheat_maize,OMN,-4.67861533164978 +wheat_maize,PAK,-0.6756108105182648 +wheat_maize,PER,-0.8993420898914337 wheat_maize,POL,7.5 -wheat_maize,PRT,-0.5709085464477539 -wheat_maize,PRY,-0.5105574131011963 -wheat_maize,PSE,1.1821171045303345 +wheat_maize,PRT,-0.5376927256584167 +wheat_maize,PRY,-0.49579864740371704 +wheat_maize,PSE,1.231361448764801 wheat_maize,ROU,7.5 wheat_maize,RUS,7.5 -wheat_maize,RWA,-0.5994908809661865 -wheat_maize,SAU,-5.082570314407349 +wheat_maize,RWA,-0.5919051170349121 +wheat_maize,SAU,-4.984448432922363 wheat_maize,SLV,-0.0 -wheat_maize,SOM,-0.052111005410552025 +wheat_maize,SOM,-0.02869903389364481 wheat_maize,SRB,7.5 wheat_maize,SSD,7.5 wheat_maize,SVK,7.5 wheat_maize,SVN,7.5 -wheat_maize,SWZ,-1.2787785530090332 -wheat_maize,SYR,-0.6548186838626862 +wheat_maize,SWZ,-1.2715407609939575 +wheat_maize,SYR,-0.6350229680538177 wheat_maize,THA,7.5 -wheat_maize,TJK,-0.8432182669639587 -wheat_maize,TKM,-0.8559079766273499 +wheat_maize,TJK,-0.8084958791732788 +wheat_maize,TKM,-0.8431630730628967 wheat_maize,TUN,-0.0 -wheat_maize,TUR,-0.11955956742167473 -wheat_maize,TWN,-0.1585310846567154 -wheat_maize,TZA,-0.36638346314430237 -wheat_maize,UGA,-1.3114354014396667 +wheat_maize,TUR,-0.07469440251588821 +wheat_maize,TWN,-0.12851351499557495 +wheat_maize,TZA,-0.3607782870531082 +wheat_maize,UGA,-1.3035997152328491 wheat_maize,UKR,7.5 -wheat_maize,URY,-0.5808399766683578 -wheat_maize,USA,-0.9632321894168854 -wheat_maize,UZB,-2.073707938194275 +wheat_maize,URY,-0.55485700070858 +wheat_maize,USA,-0.8529795408248901 +wheat_maize,UZB,-2.0486276149749756 wheat_maize,VEN,7.5 -wheat_maize,VNM,-0.5880140662193298 -wheat_maize,YEM,-1.7302067875862122 -wheat_maize,ZAF,-0.47737446427345276 -wheat_maize,ZMB,0.2719368040561676 +wheat_maize,VNM,-0.5606922507286072 +wheat_maize,YEM,-0.9536046385765076 +wheat_maize,ZAF,-0.4154755473136902 +wheat_maize,ZMB,0.29322075843811035 wheat_maize,ZWE,7.5 -wheat_soybean,AFG,0.3891846835613251 -wheat_soybean,AGO,-1.2161225080490112 +wheat_soybean,AFG,0.39053669571876526 +wheat_soybean,AGO,-1.2162240743637085 wheat_soybean,ALB,7.5 wheat_soybean,ARG,7.5 wheat_soybean,ARM,7.5 -wheat_soybean,AUS,1.3267191648483276 -wheat_soybean,AUT,4.29094934463501 -wheat_soybean,AZE,1.0549312829971313 -wheat_soybean,BDI,-1.4991952776908875 +wheat_soybean,AUS,1.3246394395828247 +wheat_soybean,AUT,4.294018983840942 +wheat_soybean,AZE,1.4846237897872925 +wheat_soybean,BDI,-1.497436285018921 wheat_soybean,BEL,7.5 -wheat_soybean,BGD,3.5746075958013535 +wheat_soybean,BGD,3.7449136255308986 wheat_soybean,BGR,7.5 wheat_soybean,BIH,7.5 wheat_soybean,BOL,7.5 -wheat_soybean,BRA,0.001530438064946793 +wheat_soybean,BRA,-0.004984307975973934 wheat_soybean,BTN,7.5 wheat_soybean,BWA,7.5 wheat_soybean,CAN,7.5 wheat_soybean,CHE,7.5 -wheat_soybean,CHN,-1.5545575022697449 -wheat_soybean,CMR,-0.564705491065979 -wheat_soybean,COD,-1.3115870356559753 +wheat_soybean,CHN,-1.3349433541297913 +wheat_soybean,CMR,-0.5575382709503174 +wheat_soybean,COD,-1.3084061741828918 wheat_soybean,CZE,7.5 wheat_soybean,DEU,7.5 wheat_soybean,DZA,-0.0 -wheat_soybean,ECU,-0.5990945100784302 -wheat_soybean,EGY,-1.7183692455291748 -wheat_soybean,ESP,0.30121442675590515 -wheat_soybean,ETH,-0.8096926808357239 +wheat_soybean,ECU,-0.5993442535400391 +wheat_soybean,EGY,-1.4095866680145264 +wheat_soybean,ESP,0.36743712425231934 +wheat_soybean,ETH,-0.8087490200996399 wheat_soybean,FRA,7.5 -wheat_soybean,GEO,0.2604876160621643 -wheat_soybean,GRC,3.496550589799881 -wheat_soybean,GTM,3.9158102571964264 -wheat_soybean,HND,-0.6383743733167648 +wheat_soybean,GEO,0.25869834423065186 +wheat_soybean,GRC,3.656270109117031 +wheat_soybean,GTM,3.917435511946678 +wheat_soybean,HND,-0.6426095217466354 wheat_soybean,HRV,7.5 -wheat_soybean,HUN,0.4032883383333683 -wheat_soybean,IND,-0.48902812600135803 -wheat_soybean,IRN,-0.008083773776888847 -wheat_soybean,IRQ,-0.4151475913822651 +wheat_soybean,HUN,-0.14069569669663906 +wheat_soybean,IND,-0.49646711349487305 +wheat_soybean,IRN,-0.007096288725733757 +wheat_soybean,IRQ,-0.4124463088810444 wheat_soybean,ISR,7.5 wheat_soybean,ITA,7.5 wheat_soybean,JOR,-0.0 -wheat_soybean,JPN,1.6944010257720947 +wheat_soybean,JPN,1.732222318649292 wheat_soybean,KAZ,7.5 -wheat_soybean,KEN,-0.7492762506008148 -wheat_soybean,KGZ,-0.16147242486476898 -wheat_soybean,KOR,3.5074272453784943 +wheat_soybean,KEN,-0.7459900677204132 +wheat_soybean,KGZ,-0.15924906730651855 +wheat_soybean,KOR,3.5098083317279816 wheat_soybean,LAO,-0.0 wheat_soybean,LBN,7.5 wheat_soybean,LSO,7.5 wheat_soybean,LUX,7.5 -wheat_soybean,MAR,-1.6725507974624634 +wheat_soybean,MAR,-1.5635004937648773 wheat_soybean,MDA,7.5 -wheat_soybean,MEX,-0.21033122390508652 +wheat_soybean,MEX,-0.23431983590126038 wheat_soybean,MKD,7.5 -wheat_soybean,MMR,3.9158369600772858 +wheat_soybean,MMR,3.9157734811306 wheat_soybean,MNE,7.5 wheat_soybean,MOZ,7.5 -wheat_soybean,MWI,3.3369496166706085 +wheat_soybean,MWI,3.3372763097286224 wheat_soybean,NGA,7.5 wheat_soybean,NLD,7.5 wheat_soybean,NPL,7.5 -wheat_soybean,PAK,-0.49255894124507904 -wheat_soybean,PER,-0.7324284911155701 +wheat_soybean,PAK,-0.4882306158542633 +wheat_soybean,PER,-0.14512190222740173 wheat_soybean,POL,7.5 wheat_soybean,PRT,7.5 -wheat_soybean,PRY,-0.1876545250415802 +wheat_soybean,PRY,-0.18735569715499878 wheat_soybean,ROU,7.5 wheat_soybean,RUS,7.5 -wheat_soybean,RWA,-0.4722822606563568 +wheat_soybean,RWA,-0.47038470208644867 wheat_soybean,SRB,7.5 wheat_soybean,SVK,7.5 wheat_soybean,SVN,7.5 -wheat_soybean,SWZ,-0.5214117765426636 +wheat_soybean,SWZ,-0.5182796120643616 wheat_soybean,SYR,7.5 wheat_soybean,THA,7.5 wheat_soybean,TJK,7.5 -wheat_soybean,TKM,4.336043536663055 +wheat_soybean,TKM,4.334611475467682 wheat_soybean,TUR,7.5 -wheat_soybean,TWN,-0.3851216435432434 -wheat_soybean,TZA,-0.43253564834594727 -wheat_soybean,UGA,-1.1988670825958252 +wheat_soybean,TWN,-0.3800724744796753 +wheat_soybean,TZA,-0.4309931993484497 +wheat_soybean,UGA,-1.1956297159194946 wheat_soybean,UKR,7.5 -wheat_soybean,URY,0.18366959691047668 -wheat_soybean,USA,0.02019089087843895 +wheat_soybean,URY,0.18772371858358383 +wheat_soybean,USA,0.06348620355129242 wheat_soybean,UZB,7.5 -wheat_soybean,VNM,-0.6542010307312012 -wheat_soybean,ZAF,-0.4300876557826996 -wheat_soybean,ZMB,-0.37386277318000793 -wheat_soybean,ZWE,4.438183784484863 +wheat_soybean,VNM,-0.6486024260520935 +wheat_soybean,ZAF,-0.32847800850868225 +wheat_soybean,ZMB,-0.36465275287628174 +wheat_soybean,ZWE,4.44290018081665 diff --git a/data/curated/calibration/gbd-anchored/provenance.yaml b/data/curated/calibration/gbd-anchored/provenance.yaml index cf8e4a1f..feb224d5 100644 --- a/data/curated/calibration/gbd-anchored/provenance.yaml +++ b/data/curated/calibration/gbd-anchored/provenance.yaml @@ -4,8 +4,8 @@ # tools/calibrate; do not edit by hand. # Licensing: see the annotation in REUSE.toml. base_config: config/gsa.yaml -generated_at: '2026-07-22T19:57:11+00:00' -git_commit: 81a69806335b9eeba703c84240982fd8ba8d131d +generated_at: '2026-07-22T21:38:01+00:00' +git_commit: ac8ebe7e7bcd34da622fb4294165ea98aa4bb063 source: gbd-anchored structural_config: aggregation.irrigated_area_source: current @@ -1007,7 +1007,6 @@ structural_config: water.irrigation.consumed_fraction: 0.58 water.irrigation.eta_max: 5.0 water.irrigation.eta_min: 0.2 - water.supply.groundwater: true water.supply.groundwater_ceiling_factor: 3.0 water.supply.pumping_cost_usd_per_m3: 0.04 water.supply.scarcity_tiers: false